query: The query consists of users’ needs, leading to several research questions that span a paragraph. Each candidate passage is an abstract from a scientific paper. The objective of this information retrieval task is to identify the abstract that most effectively meets the user’s needs in the query : It is understood that training-based models can potentially discriminate against individuals in underrepresented minority demographics. Consequently, I am keen to ensure that machine learning and deep learning models do not foster unfair representations of minority groups. This could be achieved either by introducing constraints during training or by eliminating discriminatory behaviors during model inference. Specifically, I am interested in identifying datasets where a traditionally trained model could exhibit discriminatory behavior. I am also seeking to understand specific terminologies and concepts related to this field of study. Moreover, I am looking for comprehensive evaluation benchmarks that can help determine the fairness of a model. My current hypothesis is that if we can pinpoint the decision boundary of a neural model, we can identify where discrimination occurs. If we can then adjust or modify this decision boundary, we can retroactively correct such discriminatory behaviors in models. Ultimately, my goal is to develop machine learning algorithms that are not only accurate but also fair and transparent in their treatment of protected groups.

positive:
Machine learning models are widely adopted in scenarios that directly affect
people. The development of software systems based on these models raises
societal and legal concerns, as their decisions may lead to the unfair
treatment of individuals based on attributes like race or gender. Data
preparation is key in any machine learning pipeline, but its effect on fairness
is yet to be studied in detail. In this paper, we evaluate how the fairness and
effectiveness of the learned models are affected by the removal of the
sensitive attribute, the encoding of the categorical attributes, and instance
selection methods (including cross-validators and random undersampling). We
used the Adult Income and the German Credit Data datasets, which are widely
studied and known to have fairness concerns. We applied each data preparation
technique individually to analyse the difference in predictive performance and
fairness, using statistical parity difference, disparate impact, and the
normalised prejudice index. The results show that fairness is affected by
transformations made to the training data, particularly in imbalanced datasets.
Removing the sensitive attribute is insufficient to eliminate all the
unfairness in the predictions, as expected, but it is key to achieve fairer
models. Additionally, the standard random undersampling with respect to the
true labels is sometimes more prejudicial than performing no random
undersampling.

positive:
Automated decision making based on big data and machine learning (ML)
algorithms can result in discriminatory decisions against certain protected
groups defined upon personal data like gender, race, sexual orientation etc.
Such algorithms designed to discover patterns in big data might not only pick
up any encoded societal biases in the training data, but even worse, they might
reinforce such biases resulting in more severe discrimination. The majority of
thus far proposed fairness-aware machine learning approaches focus solely on
the pre-, in- or post-processing steps of the machine learning process, that
is, input data, learning algorithms or derived models, respectively. However,
the fairness problem cannot be isolated to a single step of the ML process.
Rather, discrimination is often a result of complex interactions between big
data and algorithms, and therefore, a more holistic approach is required. The
proposed FAE (Fairness-Aware Ensemble) framework combines fairness-related
interventions at both pre- and postprocessing steps of the data analysis
process. In the preprocessing step, we tackle the problems of
under-representation of the protected group (group imbalance) and of
class-imbalance by generating balanced training samples. In the post-processing
step, we tackle the problem of class overlapping by shifting the decision
boundary in the direction of fairness.

positive:
The rise in machine learning-assisted decision-making has led to concerns
about the fairness of the decisions and techniques to mitigate problems of
discrimination. If a negative decision is made about an individual (denying a
loan, rejecting an application for housing, and so on) justice dictates that we
be able to ask how we might change circumstances to get a favorable decision
the next time. Moreover, the ability to change circumstances (a better
education, improved credentials) should not be limited to only those with
access to expensive resources. In other words, \emph{recourse} for negative
decisions should be considered a desirable value that can be equalized across
(demographically defined) groups. This paper describes how to build models that
make accurate predictions while still ensuring that the penalties for a
negative outcome do not disadvantage different groups disproportionately. We
measure recourse as the distance of an individual from the decision boundary of
a classifier. We then introduce a regularized objective to minimize the
difference in recourse across groups. We explore linear settings and further
extend recourse to non-linear settings as well as model-agnostic settings where
the exact distance from boundary cannot be calculated. Our results show that we
can successfully decrease the unfairness in recourse while maintaining
classifier performance.

positive:
Machine Learning models have been deployed across many different aspects of
society, often in situations that affect social welfare. Although these models
offer streamlined solutions to large problems, they may contain biases and
treat groups or individuals unfairly based on protected attributes such as
gender. In this paper, we introduce several examples of machine learning gender
bias in practice followed by formalizations of fairness. We provide a survey of
fairness research by detailing influential pre-processing, in-processing, and
post-processing bias mitigation algorithms. We then propose an end-to-end bias
mitigation framework, which employs a fusion of pre-, in-, and post-processing
methods to leverage the strengths of each individual technique. We test this
method, along with the standard techniques we review, on a deep neural network
to analyze bias mitigation in a deep learning setting. We find that our
end-to-end bias mitigation framework outperforms the baselines with respect to
several fairness metrics, suggesting its promise as a method for improving
fairness. As society increasingly relies on artificial intelligence to help in
decision-making, addressing gender biases present in deep learning models is
imperative. To provide readers with the tools to assess the fairness of machine
learning models and mitigate the biases present in them, we discuss multiple
open source packages for fairness in AI.

positive:
The subject of "fairness" in artificial intelligence (AI) refers to assessing
AI algorithms for potential bias based on demographic characteristics such as
race and gender, and the development of algorithms to address this bias. Most
applications to date have been in computer vision, although some work in
healthcare has started to emerge. The use of deep learning (DL) in cardiac MR
segmentation has led to impressive results in recent years, and such techniques
are starting to be translated into clinical practice. However, no work has yet
investigated the fairness of such models. In this work, we perform such an
analysis for racial/gender groups, focusing on the problem of training data
imbalance, using a nnU-Net model trained and evaluated on cine short axis
cardiac MR data from the UK Biobank dataset, consisting of 5,903 subjects from
6 different racial groups. We find statistically significant differences in
Dice performance between different racial groups. To reduce the racial bias, we
investigated three strategies: (1) stratified batch sampling, in which batch
sampling is stratified to ensure balance between racial groups; (2) fair
meta-learning for segmentation, in which a DL classifier is trained to classify
race and jointly optimized with the segmentation model; and (3) protected group
models, in which a different segmentation model is trained for each racial
group. We also compared the results to the scenario where we have a perfectly
balanced database. To assess fairness we used the standard deviation (SD) and
skewed error ratio (SER) of the average Dice values. Our results demonstrate
that the racial bias results from the use of imbalanced training data, and that
all proposed bias mitigation strategies improved fairness, with the best SD and
SER resulting from the use of protected group models.

positive:
Fairness in machine learning is crucial when individuals are subject to
automated decisions made by models in high-stake domains. Organizations that
employ these models may also need to satisfy regulations that promote
responsible and ethical A.I. While fairness metrics relying on comparing model
error rates across subpopulations have been widely investigated for the
detection and mitigation of bias, fairness in terms of the equalized ability to
achieve recourse for different protected attribute groups has been relatively
unexplored. We present a novel formulation for training neural networks that
considers the distance of data points to the decision boundary such that the
new objective: (1) reduces the average distance to the decision boundary
between two groups for individuals subject to a negative outcome in each group,
i.e. the network is more fair with respect to the ability to obtain recourse,
and (2) increases the average distance of data points to the boundary to
promote adversarial robustness. We demonstrate that training with this loss
yields more fair and robust neural networks with similar accuracies to models
trained without it. Moreover, we qualitatively motivate and empirically show
that reducing recourse disparity across groups also improves fairness measures
that rely on error rates. To the best of our knowledge, this is the first time
that recourse capabilities across groups are considered to train fairer neural
networks, and a relation between error rates based fairness and recourse based
fairness is investigated.

positive:
Context: Machine learning software can generate models that inappropriately
discriminate against specific protected social groups (e.g., groups based on
gender, ethnicity, etc). Motivated by those results, software engineering
researchers have proposed many methods for mitigating those discriminatory
effects. While those methods are effective in mitigating bias, few of them can
provide explanations on what is the root cause of bias.
  Objective: We aim at better detection and mitigation of algorithmic
discrimination in machine learning software problems.
  Method: Here we propose xFAIR, a model-based extrapolation method, that is
capable of both mitigating bias and explaining the cause. In our xFAIR
approach, protected attributes are represented by models learned from the other
independent variables (and these models offer extrapolations over the space
between existing examples). We then use the extrapolation models to relabel
protected attributes later seen in testing data or deployment time. Our
approach aims to offset the biased predictions of the classification model via
rebalancing the distribution of protected attributes.
  Results: The experiments of this paper show that, without compromising
(original) model performance, xFAIR can achieve significantly better group and
individual fairness (as measured in different metrics) than benchmark methods.
Moreover, when compared to another instance-based rebalancing method, our
model-based approach shows faster runtime and thus better scalability.
  Conclusion: Algorithmic decision bias can be removed via extrapolation that
smooths away outlier points. As evidence for this, our proposed xFAIR is not
only performance-wise better (measured by fairness and performance metrics)
than two state-of-the-art fairness algorithms.

positive:
Fairness is becoming a rising concern w.r.t. machine learning model
performance. Especially for sensitive fields such as criminal justice and loan
decision, eliminating the prediction discrimination towards a certain group of
population (characterized by sensitive features like race and gender) is
important for enhancing the trustworthiness of model. In this paper, we present
a new general framework to improve machine learning fairness. The goal of our
model is to minimize the influence of sensitive feature from the perspectives
of both the data input and the predictive model. In order to achieve this goal,
we reformulate the data input by removing the sensitive information and
strengthen model fairness by minimizing the marginal contribution of the
sensitive feature. We propose to learn the non-sensitive input via sampling
among features and design an adversarial network to minimize the dependence
between the reformulated input and the sensitive information. Extensive
experiments on three benchmark datasets suggest that our model achieve better
results than related state-of-the-art methods with respect to both fairness
metrics and prediction performance.

positive:
Data-driven algorithms play a large role in decision making across a variety
of industries. Increasingly, these algorithms are being used to make decisions
that have significant ramifications for people's social and economic
well-being, e.g. in sentencing, loan approval, and policing. Amid the
proliferation of such systems there is a growing concern about their potential
discriminatory impact. In particular, machine learning systems which are
trained on biased data have the potential to learn and perpetuate those biases.
A central challenge for practitioners is thus to determine whether their models
display discriminatory bias. Here we present a case study in which we frame the
issue of bias detection as a causal inference problem with observational data.
We enumerate two main causes of bias, sampling bias and label bias, and we
investigate the abilities of six different fairness metrics to detect each bias
type. Based on these investigations, we propose a set of best practice
guidelines to select the fairness metric that is most likely to detect bias if
it is present. Additionally, we aim to identify the conditions in which certain
fairness metrics may fail to detect bias and instead give practitioners a false
belief that their biased model is making fair decisions.

positive:
The widespread use of ML-based decision making in domains with high societal
impact such as recidivism, job hiring and loan credit has raised a lot of
concerns regarding potential discrimination. In particular, in certain cases it
has been observed that ML algorithms can provide different decisions based on
sensitive attributes such as gender or race and therefore can lead to
discrimination. Although, several fairness-aware ML approaches have been
proposed, their focus has been largely on preserving the overall classification
accuracy while improving fairness in predictions for both protected and
non-protected groups (defined based on the sensitive attribute(s)). The overall
accuracy however is not a good indicator of performance in case of class
imbalance, as it is biased towards the majority class. As we will see in our
experiments, many of the fairness-related datasets suffer from class imbalance
and therefore, tackling fairness requires also tackling the imbalance problem.
  To this end, we propose AdaFair, a fairness-aware classifier based on
AdaBoost that further updates the weights of the instances in each boosting
round taking into account a cumulative notion of fairness based upon all
current ensemble members, while explicitly tackling class-imbalance by
optimizing the number of ensemble members for balanced classification error.
Our experiments show that our approach can achieve parity in true positive and
true negative rates for both protected and non-protected groups, while it
significantly outperforms existing fairness-aware methods up to 25% in terms of
balanced error.

positive:
In recent years, automated data-driven decision-making systems have enjoyed a
tremendous success in a variety of fields (e.g., to make product
recommendations, or to guide the production of entertainment). More recently,
these algorithms are increasingly being used to assist socially sensitive
decision-making (e.g., to decide who to admit into a degree program or to
prioritize individuals for public housing). Yet, these automated tools may
result in discriminative decision-making in the sense that they may treat
individuals unfairly or unequally based on membership to a category or a
minority, resulting in disparate treatment or disparate impact and violating
both moral and ethical standards. This may happen when the training dataset is
itself biased (e.g., if individuals belonging to a particular group have
historically been discriminated upon). However, it may also happen when the
training dataset is unbiased, if the errors made by the system affect
individuals belonging to a category or minority differently (e.g., if
misclassification rates for Blacks are higher than for Whites). In this paper,
we unify the definitions of unfairness across classification and regression. We
propose a versatile mixed-integer optimization framework for learning optimal
and fair decision trees and variants thereof to prevent disparate treatment
and/or disparate impact as appropriate. This translates to a flexible schema
for designing fair and interpretable policies suitable for socially sensitive
decision-making. We conduct extensive computational studies that show that our
framework improves the state-of-the-art in the field (which typically relies on
heuristics) to yield non-discriminative decisions at lower cost to overall
accuracy.

positive:
Deep learning is increasingly being used in high-stake decision making
applications that affect individual lives. However, deep learning models might
exhibit algorithmic discrimination behaviors with respect to protected groups,
potentially posing negative impacts on individuals and society. Therefore,
fairness in deep learning has attracted tremendous attention recently. We
provide a review covering recent progresses to tackle algorithmic fairness
problems of deep learning from the computational perspective. Specifically, we
show that interpretability can serve as a useful ingredient to diagnose the
reasons that lead to algorithmic discrimination. We also discuss fairness
mitigation approaches categorized according to three stages of deep learning
life-cycle, aiming to push forward the area of fairness in deep learning and
build genuinely fair and reliable deep learning systems.

positive:
Machine learning algorithms are increasingly used for consequential decision
making regarding individuals based on their relevant features. Features that
are relevant for accurate decisions may however lead to either explicit or
implicit forms of discrimination against unprivileged groups, such as those of
certain race or gender. This happens due to existing biases in the training
data, which are often replicated or even exacerbated by the learning algorithm.
Identifying and measuring these biases at the data level is a challenging
problem due to the interdependence among the features, and the decision
outcome. In this work, we develop a framework for fairness-aware feature
selection which takes into account the correlation among the features and the
decision outcome, and is based on information theoretic measures for the
accuracy and discriminatory impacts of features. In particular, we first
propose information theoretic measures which quantify the impact of different
subsets of features on the accuracy and discrimination of the decision
outcomes. We then deduce the marginal impact of each feature using Shapley
value function; a solution concept in cooperative game theory used to estimate
marginal contributions of players in a coalitional game. Finally, we design a
fairness utility score for each feature (for feature selection) which
quantifies how this feature influences accurate as well as nondiscriminatory
decisions. Our framework depends on the joint statistics of the data rather
than a particular classifier design. We examine our proposed framework on real
and synthetic data to evaluate its performance.

positive:
Data-driven decision making is gaining prominence with the popularity of
various machine learning models. Unfortunately, real-life data used in machine
learning training may capture human biases, and as a result the learned models
may lead to unfair decision making. In this paper, we provide a solution to
this problem for decision trees and random forests. Our approach converts any
decision tree or random forest into a fair one with respect to a specific data
set, fairness criteria, and sensitive attributes. The \emph{FairRepair} tool,
built based on our approach, is inspired by automated program repair techniques
for traditional programs. It uses an SMT solver to decide which paths in the
decision tree could have their outcomes flipped to improve the fairness of the
model. Our experiments on the well-known adult dataset from UC Irvine
demonstrate that FairRepair scales to realistic decision trees and random
forests. Furthermore, FairRepair provides formal guarantees about soundness and
completeness of finding a repair. Since our fairness-guided repair technique
repairs decision trees and random forests obtained from a given (unfair)
data-set, it can help to identify and rectify biases in decision-making in an
organisation.

positive:
Algorithmic decision making based on computer vision and machine learning
technologies continue to permeate our lives. But issues related to biases of
these models and the extent to which they treat certain segments of the
population unfairly, have led to concern in the general public. It is now
accepted that because of biases in the datasets we present to the models, a
fairness-oblivious training will lead to unfair models. An interesting topic is
the study of mechanisms via which the de novo design or training of the model
can be informed by fairness measures. Here, we study mechanisms that impose
fairness concurrently while training the model. While existing fairness based
approaches in vision have largely relied on training adversarial modules
together with the primary classification/regression task, in an effort to
remove the influence of the protected attribute or variable, we show how ideas
based on well-known optimization concepts can provide a simpler alternative. In
our proposed scheme, imposing fairness just requires specifying the protected
attribute and utilizing our optimization routine. We provide a detailed
technical analysis and present experiments demonstrating that various fairness
measures from the literature can be reliably imposed on a number of training
tasks in vision in a manner that is interpretable.

positive:
Machine learning models are increasingly being used in important
decision-making software such as approving bank loans, recommending criminal
sentencing, hiring employees, and so on. It is important to ensure the fairness
of these models so that no discrimination is made based on protected attribute
(e.g., race, sex, age) while decision making. Algorithms have been developed to
measure unfairness and mitigate them to a certain extent. In this paper, we
have focused on the empirical evaluation of fairness and mitigations on
real-world machine learning models. We have created a benchmark of 40 top-rated
models from Kaggle used for 5 different tasks, and then using a comprehensive
set of fairness metrics, evaluated their fairness. Then, we have applied 7
mitigation techniques on these models and analyzed the fairness, mitigation
results, and impacts on performance. We have found that some model optimization
techniques result in inducing unfairness in the models. On the other hand,
although there are some fairness control mechanisms in machine learning
libraries, they are not documented. The mitigation algorithm also exhibit
common patterns such as mitigation in the post-processing is often costly (in
terms of performance) and mitigation in the pre-processing stage is preferred
in most cases. We have also presented different trade-off choices of fairness
mitigation decisions. Our study suggests future research directions to reduce
the gap between theoretical fairness aware algorithms and the software
engineering methods to leverage them in practice.

positive:
Machine Learning (ML) models trained on data from multiple demographic groups
can inherit representation disparity (Hashimoto et al., 2018) that may exist in
the data: the model may be less favorable to groups contributing less to the
training process; this in turn can degrade population retention in these groups
over time, and exacerbate representation disparity in the long run. In this
study, we seek to understand the interplay between ML decisions and the
underlying group representation, how they evolve in a sequential framework, and
how the use of fairness criteria plays a role in this process. We show that the
representation disparity can easily worsen over time under a natural user
dynamics (arrival and departure) model when decisions are made based on a
commonly used objective and fairness criteria, resulting in some groups
diminishing entirely from the sample pool in the long run. It highlights the
fact that fairness criteria have to be defined while taking into consideration
the impact of decisions on user dynamics. Toward this end, we explain how a
proper fairness criterion can be selected based on a general user dynamics
model.

positive:
As more industries integrate machine learning into socially sensitive
decision processes like hiring, loan-approval, and parole-granting, we are at
risk of perpetuating historical and contemporary socioeconomic disparities.
This is a critical problem because on the one hand, organizations who use but
do not understand the discriminatory potential of such systems will facilitate
the widening of social disparities under the assumption that algorithms are
categorically objective. On the other hand, the responsible use of machine
learning can help us measure, understand, and mitigate the implicit historical
biases in socially sensitive data by expressing implicit decision-making mental
models in terms of explicit statistical models. In this paper we specify,
implement, and evaluate a "fairness-aware" machine learning interface called
themis-ml, which is intended for use by individual data scientists and
engineers, academic research teams, or larger product teams who use machine
learning in production systems.

positive:
Discrimination is a focal concern in supervised learning algorithms
augmenting human decision-making. These systems are trained using historical
data, which may have been tainted by discrimination, and may learn biases
against the protected groups. An important question is how to train models
without propagating discrimination. In this study, we i) define and model
discrimination as perturbations of a data-generating process and show how
discrimination can be induced via attributes correlated with the protected
attributes; ii) introduce a measure of resilience of a supervised learning
algorithm to potentially discriminatory data perturbations, iii) propose a
novel supervised learning algorithm that inhibits discrimination, and iv) show
that it is more resilient to discriminatory perturbations in synthetic and
real-world datasets than state-of-the-art learning algorithms. The proposed
method can be used with general supervised learning algorithms and avoids
inducement of discrimination, while maximizing model accuracy.

positive:
Artificial neural networks perform state-of-the-art in an ever-growing number
of tasks, nowadays they are used to solve an incredibly large variety of tasks.
However, typical training strategies do not take into account lawful, ethical
and discriminatory potential issues the trained ANN models could incur in. In
this work we propose NDR, a non-discriminatory regularization strategy to
prevent the ANN model to solve the target task using some discriminatory
features like, for example, the ethnicity in an image classification task for
human faces. In particular, a part of the ANN model is trained to hide the
discriminatory information such that the rest of the network focuses in
learning the given learning task. Our experiments show that NDR can be
exploited to achieve non-discriminatory models with both minimal computational
overhead and performance loss.

positive:
Automated decision making systems are increasingly being used in real-world
applications. In these systems for the most part, the decision rules are
derived by minimizing the training error on the available historical data.
Therefore, if there is a bias related to a sensitive attribute such as gender,
race, religion, etc. in the data, say, due to cultural/historical
discriminatory practices against a certain demographic, the system could
continue discrimination in decisions by including the said bias in its decision
rule. We present an information theoretic framework for designing fair
predictors from data, which aim to prevent discrimination against a specified
sensitive attribute in a supervised learning setting. We use equalized odds as
the criterion for discrimination, which demands that the prediction should be
independent of the protected attribute conditioned on the actual label. To
ensure fairness and generalization simultaneously, we compress the data to an
auxiliary variable, which is used for the prediction task. This auxiliary
variable is chosen such that it is decontaminated from the discriminatory
attribute in the sense of equalized odds. The final predictor is obtained by
applying a Bayesian decision rule to the auxiliary variable.

positive:
Artificial intelligence nowadays plays an increasingly prominent role in our
life since decisions that were once made by humans are now delegated to
automated systems. A machine learning algorithm trained based on biased data,
however, tends to make unfair predictions. Developing classification algorithms
that are fair with respect to protected attributes of the data thus becomes an
important problem. Motivated by concerns surrounding the fairness effects of
sharing and few-shot machine learning tools, such as the Model Agnostic
Meta-Learning framework, we propose a novel fair fast-adapted few-shot
meta-learning approach that efficiently mitigates biases during meta-train by
ensuring controlling the decision boundary covariance that between the
protected variable and the signed distance from the feature vectors to the
decision boundary. Through extensive experiments on two real-world image
benchmarks over three state-of-the-art meta-learning algorithms, we empirically
demonstrate that our proposed approach efficiently mitigates biases on model
output and generalizes both accuracy and fairness to unseen tasks with a
limited amount of training samples.

positive:
Considerable research effort has been guided towards algorithmic fairness but
real-world adoption of bias reduction techniques is still scarce. Existing
methods are either metric- or model-specific, require access to sensitive
attributes at inference time, or carry high development or deployment costs.
This work explores the unfairness that emerges when optimizing ML models solely
for predictive performance, and how to mitigate it with a simple and easily
deployed intervention: fairness-aware hyperparameter optimization (HO). We
propose and evaluate fairness-aware variants of three popular HO algorithms:
Fair Random Search, Fair TPE, and Fairband. We validate our approach on a
real-world bank account opening fraud case-study, as well as on three datasets
from the fairness literature. Results show that, without extra training cost,
it is feasible to find models with 111% mean fairness increase and just 6%
decrease in performance when compared with fairness-blind HO.

positive:
Machine learning models are becoming pervasive in high-stakes applications.
Despite their clear benefits in terms of performance, the models could show
discrimination against minority groups and result in fairness issues in a
decision-making process, leading to severe negative impacts on the individuals
and the society. In recent years, various techniques have been developed to
mitigate the unfairness for machine learning models. Among them, in-processing
methods have drawn increasing attention from the community, where fairness is
directly taken into consideration during model design to induce intrinsically
fair models and fundamentally mitigate fairness issues in outputs and
representations. In this survey, we review the current progress of
in-processing fairness mitigation techniques. Based on where the fairness is
achieved in the model, we categorize them into explicit and implicit methods,
where the former directly incorporates fairness metrics in training objectives,
and the latter focuses on refining latent representation learning. Finally, we
conclude the survey with a discussion of the research challenges in this
community to motivate future exploration.

positive:
In recent years, machine learning techniques have been increasingly applied
in sensitive decision making processes, raising fairness concerns. Past
research has shown that machine learning may reproduce and even exacerbate
human bias due to biased training data or flawed model assumptions, and thus
may lead to discriminatory actions. To counteract such biased models,
researchers have proposed multiple mathematical definitions of fairness
according to which classifiers can be optimized. However, it has also been
shown that the outcomes generated by some fairness notions may be
unsatisfactory.
  In this contribution, we add to this research by considering decision making
processes in time. We establish a theoretic model in which even perfectly
accurate classifiers which adhere to almost all common fairness definitions
lead to stable long-term inequalities due to vicious cycles. Only demographic
parity, which enforces equal rates of positive decisions across groups, avoids
these effects and establishes a virtuous cycle, which leads to perfectly
accurate and fair classification in the long term.

positive:
Recent work in fairness in machine learning has proposed adjusting for
fairness by equalizing accuracy metrics across groups and has also studied how
datasets affected by historical prejudices may lead to unfair decision
policies. We connect these lines of work and study the residual unfairness that
arises when a fairness-adjusted predictor is not actually fair on the target
population due to systematic censoring of training data by existing biased
policies. This scenario is particularly common in the same applications where
fairness is a concern. We characterize theoretically the impact of such
censoring on standard fairness metrics for binary classifiers and provide
criteria for when residual unfairness may or may not appear. We prove that,
under certain conditions, fairness-adjusted classifiers will in fact induce
residual unfairness that perpetuates the same injustices, against the same
groups, that biased the data to begin with, thus showing that even
state-of-the-art fair machine learning can have a "bias in, bias out" property.
When certain benchmark data is available, we show how sample reweighting can
estimate and adjust fairness metrics while accounting for censoring. We use
this to study the case of Stop, Question, and Frisk (SQF) and demonstrate that
attempting to adjust for fairness perpetuates the same injustices that the
policy is infamous for.

positive:
Explicit and implicit bias clouds human judgement, leading to discriminatory
treatment of minority groups. A fundamental goal of algorithmic fairness is to
avoid the pitfalls in human judgement by learning policies that improve the
overall outcomes while providing fair treatment to protected classes. In this
paper, we propose a causal framework that learns optimal intervention policies
from data subject to fairness constraints. We define two measures of treatment
bias and infer best treatment assignment that minimizes the bias while
optimizing overall outcome. We demonstrate that there is a dilemma of balancing
fairness and overall benefit; however, allowing preferential treatment to
protected classes in certain circumstances (affirmative action) can
dramatically improve the overall benefit while also preserving fairness. We
apply our framework to data containing student outcomes on standardized tests
and show how it can be used to design real-world policies that fairly improve
student test scores. Our framework provides a principled way to learn fair
treatment policies in real-world settings.

positive:
Software often produces biased outputs. In particular, machine learning (ML)
based software are known to produce erroneous predictions when processing
discriminatory inputs. Such unfair program behavior can be caused by societal
bias. In the last few years, Amazon, Microsoft and Google have provided
software services that produce unfair outputs, mostly due to societal bias
(e.g. gender or race). In such events, developers are saddled with the task of
conducting fairness testing. Fairness testing is challenging; developers are
tasked with generating discriminatory inputs that reveal and explain biases.
  We propose a grammar-based fairness testing approach (called ASTRAEA) which
leverages context-free grammars to generate discriminatory inputs that reveal
fairness violations in software systems. Using probabilistic grammars, ASTRAEA
also provides fault diagnosis by isolating the cause of observed software bias.
ASTRAEA's diagnoses facilitate the improvement of ML fairness.
  ASTRAEA was evaluated on 18 software systems that provide three major natural
language processing (NLP) services. In our evaluation, ASTRAEA generated
fairness violations with a rate of ~18%. ASTRAEA generated over 573K
discriminatory test cases and found over 102K fairness violations. Furthermore,
ASTRAEA improves software fairness by ~76%, via model-retraining.

positive:
Training classifiers under fairness constraints such as group fairness,
regularizes the disparities of predictions between the groups. Nevertheless,
even though the constraints are satisfied during training, they might not
generalize at evaluation time. To improve the generalizability of fair
classifiers, we propose fair mixup, a new data augmentation strategy for
imposing the fairness constraint. In particular, we show that fairness can be
achieved by regularizing the models on paths of interpolated samples between
the groups. We use mixup, a powerful data augmentation strategy to generate
these interpolates. We analyze fair mixup and empirically show that it ensures
a better generalization for both accuracy and fairness measurement in tabular,
vision, and language benchmarks.

positive:
The discovery of discriminatory bias in human or automated decision making is
a task of increasing importance and difficulty, exacerbated by the pervasive
use of machine learning and data mining. Currently, discrimination discovery
largely relies upon correlation analysis of decisions records, disregarding the
impact of confounding biases. We present a method for causal discrimination
discovery based on propensity score analysis, a statistical tool for filtering
out the effect of confounding variables. We introduce causal measures of
discrimination which quantify the effect of group membership on the decisions,
and highlight causal discrimination/favoritism patterns by learning regression
trees over the novel measures. We validate our approach on two real world
datasets. Our proposed framework for causal discrimination has the potential to
enhance the transparency of machine learning with tools for detecting
discriminatory bias both in the training data and in the learning algorithms.

positive:
Classification, a heavily-studied data-driven machine learning task, drives
an increasing number of prediction systems involving critical human decisions
such as loan approval and criminal risk assessment. However, classifiers often
demonstrate discriminatory behavior, especially when presented with biased
data. Consequently, fairness in classification has emerged as a high-priority
research area. Data management research is showing an increasing presence and
interest in topics related to data and algorithmic fairness, including the
topic of fair classification. The interdisciplinary efforts in fair
classification, with machine learning research having the largest presence,
have resulted in a large number of fairness notions and a wide range of
approaches that have not been systematically evaluated and compared. In this
paper, we contribute a broad analysis of 13 fair classification approaches and
additional variants, over their correctness, fairness, efficiency, scalability,
robustness to data errors, sensitivity to underlying ML model, data efficiency,
and stability using a variety of metrics and real-world datasets. Our analysis
highlights novel insights on the impact of different metrics and high-level
approach characteristics on different aspects of performance. We also discuss
general principles for choosing approaches suitable for different practical
settings, and identify areas where data-management-centric solutions are likely
to have the most impact.

positive:
Recent research has helped to cultivate growing awareness that machine
learning systems fueled by big data can create or exacerbate troubling
disparities in society. Much of this research comes from outside of the
practicing data science community, leaving its members with little concrete
guidance to proactively address these concerns. This article introduces issues
of discrimination to the data science community on its own terms. In it, we
tour the familiar data mining process while providing a taxonomy of common
practices that have the potential to produce unintended discrimination. We also
survey how discrimination is commonly measured, and suggest how familiar
development processes can be augmented to mitigate systems' discriminatory
potential. We advocate that data scientists should be intentional about
modeling and reducing discriminatory outcomes. Without doing so, their efforts
will result in perpetuating any systemic discrimination that may exist, but
under a misleading veil of data-driven objectivity.

positive:
We study fairness in supervised few-shot meta-learning models that are
sensitive to discrimination (or bias) in historical data. A machine learning
model trained based on biased data tends to make unfair predictions for users
from minority groups. Although this problem has been studied before, existing
methods mainly aim to detect and control the dependency effect of the protected
variables (e.g. race, gender) on target prediction based on a large amount of
training data. These approaches carry two major drawbacks that (1) lacking
showing a global cause-effect visualization for all variables; (2) lacking
generalization of both accuracy and fairness to unseen tasks. In this work, we
first discover discrimination from data using a causal Bayesian knowledge graph
which not only demonstrates the dependency of the protected variable on target
but also indicates causal effects between all variables. Next, we develop a
novel algorithm based on risk difference in order to quantify the
discriminatory influence for each protected variable in the graph. Furthermore,
to protect prediction from unfairness, a fast-adapted bias-control approach in
meta-learning is proposed, which efficiently mitigates statistical disparity
for each task and it thus ensures independence of protected attributes on
predictions based on biased and few-shot data samples. Distinct from existing
meta-learning models, group unfairness of tasks are efficiently reduced by
leveraging the mean difference between (un)protected groups for regression
problems. Through extensive experiments on both synthetic and real-world data
sets, we demonstrate that our proposed unfairness discovery and prevention
approaches efficiently detect discrimination and mitigate biases on model
output as well as generalize both accuracy and fairness to unseen tasks with a
limited amount of training samples.

positive:
Nowadays, many decisions are made using predictive models built on historical
data.Predictive models may systematically discriminate groups of people even if
the computing process is fair and well-intentioned. Discrimination-aware data
mining studies how to make predictive models free from discrimination, when
historical data, on which they are built, may be biased, incomplete, or even
contain past discriminatory decisions. Discrimination refers to disadvantageous
treatment of a person based on belonging to a category rather than on
individual merit. In this survey we review and organize various discrimination
measures that have been used for measuring discrimination in data, as well as
in evaluating performance of discrimination-aware predictive models. We also
discuss related measures from other disciplines, which have not been used for
measuring discrimination, but potentially could be suitable for this purpose.
We computationally analyze properties of selected measures. We also review and
discuss measuring procedures, and present recommendations for practitioners.
The primary target audience is data mining, machine learning, pattern
recognition, statistical modeling researchers developing new methods for
non-discriminatory predictive modeling. In addition, practitioners and policy
makers would use the survey for diagnosing potential discrimination by
predictive models.

positive:
Although deep learning has demonstrated astonishing performance in many
applications, there are still concerns about its dependability. One desirable
property of deep learning applications with societal impact is fairness (i.e.,
non-discrimination). Unfortunately, discrimination might be intrinsically
embedded into the models due to the discrimination in the training data. As a
countermeasure, fairness testing systemically identifies discriminatory
samples, which can be used to retrain the model and improve the model's
fairness. Existing fairness testing approaches however have two major
limitations. Firstly, they only work well on traditional machine learning
models and have poor performance (e.g., effectiveness and efficiency) on deep
learning models. Secondly, they only work on simple structured (e.g., tabular)
data and are not applicable for domains such as text. In this work, we bridge
the gap by proposing a scalable and effective approach for systematically
searching for discriminatory samples while extending existing fairness testing
approaches to address a more challenging domain, i.e., text classification.
Compared with state-of-the-art methods, our approach only employs lightweight
procedures like gradient computation and clustering, which is significantly
more scalable and effective. Experimental results show that on average, our
approach explores the search space much more effectively (9.62 and 2.38 times
more than the state-of-the-art methods respectively on tabular and text
datasets) and generates much more discriminatory samples (24.95 and 2.68 times)
within a same reasonable time. Moreover, the retrained models reduce
discrimination by 57.2% and 60.2% respectively on average.

positive:
The Fairness, Accountability, and Transparency in Machine Learning (FAT-ML)
literature proposes a varied set of group fairness metrics to measure
discrimination against socio-demographic groups that are characterized by a
protected feature, such as gender or race.Such a system can be deemed as either
fair or unfair depending on the choice of the metric. Several metrics have been
proposed, some of them incompatible with each other.We do so empirically, by
observing that several of these metrics cluster together in two or three main
clusters for the same groups and machine learning methods. In addition, we
propose a robust way to visualize multidimensional fairness in two dimensions
through a Principal Component Analysis (PCA) of the group fairness metrics.
Experimental results on multiple datasets show that the PCA decomposition
explains the variance between the metrics with one to three components.

positive:
Machine learning models are extensively being used to make decisions that
have a significant impact on human life. These models are trained over
historical data that may contain information about sensitive attributes such as
race, sex, religion, etc. The presence of such sensitive attributes can impact
certain population subgroups unfairly. It is straightforward to remove
sensitive features from the data; however, a model could pick up prejudice from
latent sensitive attributes that may exist in the training data. This has led
to the growing apprehension about the fairness of the employed models. In this
paper, we propose a novel algorithm that can effectively identify and treat
latent discriminating features. The approach is agnostic of the learning
algorithm and generalizes well for classification as well as regression tasks.
It can also be used as a key aid in proving that the model is free of
discrimination towards regulatory compliance if the need arises. The approach
helps to collect discrimination-free features that would improve the model
performance while ensuring the fairness of the model. The experimental results
from our evaluations on publicly available real-world datasets show a
near-ideal fairness measurement in comparison to other methods.

positive:
Machine learning and data mining algorithms have been increasingly used
recently to support decision-making systems in many areas of high societal
importance such as healthcare, education, or security. While being very
efficient in their predictive abilities, the deployed algorithms sometimes tend
to learn an inductive model with a discriminative bias due to the presence of
this latter in the learning sample. This problem gave rise to a new field of
algorithmic fairness where the goal is to correct the discriminative bias
introduced by a certain attribute in order to decorrelate it from the model's
output. In this paper, we study the problem of fairness for the task of edge
prediction in graphs, a largely underinvestigated scenario compared to a more
popular setting of fair classification. To this end, we formulate the problem
of fair edge prediction, analyze it theoretically, and propose an
embedding-agnostic repairing procedure for the adjacency matrix of an arbitrary
graph with a trade-off between the group and individual fairness. We
experimentally show the versatility of our approach and its capacity to provide
explicit control over different notions of fairness and prediction accuracy.

positive:
Effective machine learning models can automatically learn useful information
from a large quantity of data and provide decisions in a high accuracy. These
models may, however, lead to unfair predictions in certain sense among the
population groups of interest, where the grouping is based on such sensitive
attributes as race and gender. Various fairness definitions, such as
demographic parity and equalized odds, were proposed in prior art to ensure
that decisions guided by the machine learning models are equitable.
Unfortunately, the "fair" model trained with these fairness definitions is
threshold sensitive, i.e., the condition of fairness may no longer hold true
when tuning the decision threshold. This paper introduces the notion of
threshold invariant fairness, which enforces equitable performances across
different groups independent of the decision threshold. To achieve this goal,
this paper proposes to equalize the risk distributions among the groups via two
approximation methods. Experimental results demonstrate that the proposed
methodology is effective to alleviate the threshold sensitivity in machine
learning models designed to achieve fairness.

positive:
Growing use of machine learning in policy and social impact settings have
raised concerns for fairness implications, especially for racial minorities.
These concerns have generated considerable interest among machine learning and
artificial intelligence researchers, who have developed new methods and
established theoretical bounds for improving fairness, focusing on the source
data, regularization and model training, or post-hoc adjustments to model
scores. However, little work has studied the practical trade-offs between
fairness and accuracy in real-world settings to understand how these bounds and
methods translate into policy choices and impact on society. Our empirical
study fills this gap by investigating the impact of mitigating disparities on
accuracy, focusing on the common context of using machine learning to inform
benefit allocation in resource-constrained programs across education, mental
health, criminal justice, and housing safety. Here we describe applied work in
which we find fairness-accuracy trade-offs to be negligible in practice. In
each setting studied, explicitly focusing on achieving equity and using our
proposed post-hoc disparity mitigation methods, fairness was substantially
improved without sacrificing accuracy. This observation was robust across
policy contexts studied, scale of resources available for intervention, time,
and relative size of the protected groups. These empirical results challenge a
commonly held assumption that reducing disparities either requires accepting an
appreciable drop in accuracy or the development of novel, complex methods,
making reducing disparities in these applications more practical.

positive:
Fair machine learning is receiving an increasing attention in machine
learning fields. Researchers in fair learning have developed correlation or
association-based measures such as demographic disparity, mistreatment
disparity, calibration, causal-based measures such as total effect, direct and
indirect discrimination, and counterfactual fairness, and fairness notions such
as equality of opportunity and equal odds that consider both decisions in the
training data and decisions made by predictive models. In this paper, we
develop a new causal-based fairness notation, called equality of effort.
Different from existing fairness notions which mainly focus on discovering the
disparity of decisions between two groups of individuals, the proposed equality
of effort notation helps answer questions like to what extend a legitimate
variable should change to make a particular individual achieve a certain
outcome level and addresses the concerns whether the efforts made to achieve
the same outcome level for individuals from the protected group and that from
the unprotected group are different. We develop algorithms for determining
whether an individual or a group of individuals is discriminated in terms of
equality of effort. We also develop an optimization-based method for removing
discriminatory effects from the data if discrimination is detected. We conduct
empirical evaluations to compare the equality of effort and existing fairness
notion and show the effectiveness of our proposed algorithms.

positive:
Representation Learning in a heterogeneous space with mixed variables of
numerical and categorical types has interesting challenges due to its complex
feature manifold. Moreover, feature learning in an unsupervised setup, without
class labels and a suitable learning loss function, adds to the problem
complexity. Further, the learned representation and subsequent predictions
should not reflect discriminatory behavior towards certain sensitive groups or
attributes. The proposed feature map should preserve maximum variations present
in the data and needs to be fair with respect to the sensitive variables. We
propose, in the first phase of our work, an efficient encoder-decoder framework
to capture the mixed-domain information. The second phase of our work focuses
on de-biasing the mixed space representations by adding relevant fairness
constraints. This ensures minimal information loss between the representations
before and after the fairness-preserving projections. Both the information
content and the fairness aspect of the final representation learned has been
validated through several metrics where it shows excellent performance. Our
work (FairMixRep) addresses the problem of Mixed Space Fair Representation
learning from an unsupervised perspective and learns a Universal representation
that is timely, unique, and a novel research contribution.

positive:
We study fairness in collaborative-filtering recommender systems, which are
sensitive to discrimination that exists in historical data. Biased data can
lead collaborative filtering methods to make unfair predictions against
minority groups of users. We identify the insufficiency of existing fairness
metrics and propose four new metrics that address different forms of
unfairness. These fairness metrics can be optimized by adding fairness terms to
the learning objective. Experiments on synthetic and real data show that our
new metrics can better measure fairness than the baseline, and that the
fairness objectives effectively help reduce unfairness.

positive:
Thanks to the increasing growth of computational power and data availability,
the research in machine learning has advanced with tremendous rapidity.
Nowadays, the majority of automatic decision making systems are based on data.
However, it is well known that machine learning systems can present problematic
results if they are built on partial or incomplete data. In fact, in recent
years several studies have found a convergence of issues related to the ethics
and transparency of these systems in the process of data collection and how
they are recorded. Although the process of rigorous data collection and
analysis is fundamental in the model design, this step is still largely
overlooked by the machine learning community. For this reason, we propose a
method of data annotation based on Bayesian statistical inference that aims to
warn about the risk of discriminatory results of a given data set. In
particular, our method aims to deepen knowledge and promote awareness about the
sampling practices employed to create the training set, highlighting that the
probability of success or failure conditioned to a minority membership is given
by the structure of the data available. We empirically test our system on three
datasets commonly accessed by the machine learning community and we investigate
the risk of racial discrimination.

positive:
Machine learning is being integrated into a growing number of critical
systems with far-reaching impacts on society. Unexpected behaviour and unfair
decision processes are coming under increasing scrutiny due to this widespread
use and its theoretical considerations. Individuals, as well as organisations,
notice, test, and criticize unfair results to hold model designers and
deployers accountable. We offer a framework that assists these groups in
mitigating unfair representations stemming from the training datasets. Our
framework relies on two inter-operating adversaries to improve fairness. First,
a model is trained with the goal of preventing the guessing of protected
attributes' values while limiting utility losses. This first step optimizes the
model's parameters for fairness. Second, the framework leverages evasion
attacks from adversarial machine learning to generate new examples that will be
misclassified. These new examples are then used to retrain and improve the
model in the first step. These two steps are iteratively applied until a
significant improvement in fairness is obtained. We evaluated our framework on
well-studied datasets in the fairness literature -- including COMPAS -- where
it can surpass other approaches concerning demographic parity, equality of
opportunity and also the model's utility. We also illustrate our findings on
the subtle difficulties when mitigating unfairness and highlight how our
framework can assist model designers.

positive:
Algorithms learned from data are increasingly used for deciding many aspects
in our life: from movies we see, to prices we pay, or medicine we get. Yet
there is growing evidence that decision making by inappropriately trained
algorithms may unintentionally discriminate people. For example, in automated
matching of candidate CVs with job descriptions, algorithms may capture and
propagate ethnicity related biases. Several repairs for selected algorithms
have already been proposed, but the underlying mechanisms how such
discrimination happens from the computational perspective are not yet
scientifically understood. We need to develop theoretical understanding how
algorithms may become discriminatory, and establish fundamental machine
learning principles for prevention. We need to analyze machine learning process
as a whole to systematically explain the roots of discrimination occurrence,
which will allow to devise global machine learning optimization criteria for
guaranteed prevention, as opposed to pushing empirical constraints into
existing algorithms case-by-case. As a result, the state-of-the-art will
advance from heuristic repairing, to proactive and theoretically supported
prevention. This is needed not only because law requires to protect vulnerable
people. Penetration of big data initiatives will only increase, and computer
science needs to provide solid explanations and accountability to the public,
before public concerns lead to unnecessarily restrictive regulations against
machine learning.

positive:
Thanks to the great progress of machine learning in the last years, several
Artificial Intelligence (AI) techniques have been increasingly moving from the
controlled research laboratory settings to our everyday life. AI is clearly
supportive in many decision-making scenarios, but when it comes to sensitive
areas such as health care, hiring policies, education, banking or justice, with
major impact on individuals and society, it becomes crucial to establish
guidelines on how to design, develop, deploy and monitor this technology.
Indeed the decision rules elaborated by machine learning models are data-driven
and there are multiple ways in which discriminatory biases can seep into data.
Algorithms trained on those data incur the risk of amplifying prejudices and
societal stereotypes by over associating protected attributes such as gender,
ethnicity or disabilities with the prediction task. Starting from the extensive
experience of the National Metrology Institute on measurement standards and
certification roadmaps, and of Politecnico di Torino on machine learning as
well as methods for domain bias evaluation and mastering, we propose a first
joint effort to define the operational steps needed for AI fairness
certification. Specifically we will overview the criteria that should be met by
an AI system before coming into official service and the conformity assessment
procedures useful to monitor its functioning for fair decisions.

positive:
The lack of bias management in Recommender Systems leads to minority groups
receiving unfair recommendations. Moreover, the trade-off between equity and
precision makes it difficult to obtain recommendations that meet both criteria.
Here we propose a Deep Learning based Collaborative Filtering algorithm that
provides recommendations with an optimum balance between fairness and accuracy
without knowing demographic information about the users. Experimental results
show that it is possible to make fair recommendations without losing a
significant proportion of accuracy.

positive:
Decision making in crucial applications such as lending, hiring, and college
admissions has witnessed increasing use of algorithmic models and techniques as
a result of a confluence of factors such as ubiquitous connectivity, ability to
collect, aggregate, and process large amounts of fine-grained data using cloud
computing, and ease of access to applying sophisticated machine learning
models. Quite often, such applications are powered by search and recommendation
systems, which in turn make use of personalized ranking algorithms. At the same
time, there is increasing awareness about the ethical and legal challenges
posed by the use of such data-driven systems. Researchers and practitioners
from different disciplines have recently highlighted the potential for such
systems to discriminate against certain population groups, due to biases in the
datasets utilized for learning their underlying recommendation models. We
present a study of fairness in online personalization settings involving the
ranking of individuals. Starting from a fair warm-start machine-learned model,
we first demonstrate that online personalization can cause the model to learn
to act in an unfair manner if the user is biased in his/her responses. For this
purpose, we construct a stylized model for generating training data with
potentially biased features as well as potentially biased labels and quantify
the extent of bias that is learned by the model when the user responds in a
biased manner as in many real-world scenarios. We then formulate the problem of
learning personalized models under fairness constraints and present a
regularization based approach for mitigating biases in machine learning. We
demonstrate the efficacy of our approach through extensive simulations with
different parameter settings. Code:
https://github.com/groshanlal/Fairness-Aware-Online-Personalization

positive:
Fairness is a critical trait in decision making. As machine-learning models
are increasingly being used in sensitive application domains (e.g. education
and employment) for decision making, it is crucial that the decisions computed
by such models are free of unintended bias. But how can we automatically
validate the fairness of arbitrary machine-learning models? For a given
machine-learning model and a set of sensitive input parameters, our AEQUITAS
approach automatically discovers discriminatory inputs that highlight fairness
violation. At the core of AEQUITAS are three novel strategies to employ
probabilistic search over the input space with the objective of uncovering
fairness violation. Our AEQUITAS approach leverages inherent robustness
property in common machine-learning models to design and implement scalable
test generation methodologies. An appealing feature of our generated test
inputs is that they can be systematically added to the training set of the
underlying model and improve its fairness. To this end, we design a fully
automated module that guarantees to improve the fairness of the underlying
model.
  We implemented AEQUITAS and we have evaluated it on six state-of-the-art
classifiers, including a classifier that was designed with fairness
constraints. We show that AEQUITAS effectively generates inputs to uncover
fairness violation in all the subject classifiers and systematically improves
the fairness of the respective models using the generated test inputs. In our
evaluation, AEQUITAS generates up to 70% discriminatory inputs (w.r.t. the
total number of inputs generated) and leverages these inputs to improve the
fairness up to 94%.

positive:
Machine learning systems are often trained using data collected from
historical decisions. If past decisions were biased, then automated systems
that learn from historical data will also be biased. We propose a black-box
approach to identify and remove biased training data. Machine learning models
trained on such debiased data (a subset of the original training data) have low
individual discrimination, often 0%. These models also have greater accuracy
and lower statistical disparity than models trained on the full historical
data. We evaluated our methodology in experiments using 6 real-world datasets.
Our approach outperformed seven previous approaches in terms of individual
discrimination and accuracy.

positive:
The popularity of pretrained language models in natural language processing
systems calls for a careful evaluation of such models in down-stream tasks,
which have a higher potential for societal impact. The evaluation of such
systems usually focuses on accuracy measures. Our findings in this paper call
for attention to be paid to fairness measures as well. Through the analysis of
more than a dozen pretrained language models of varying sizes on two toxic text
classification tasks (English), we demonstrate that focusing on accuracy
measures alone can lead to models with wide variation in fairness
characteristics. Specifically, we observe that fairness can vary even more than
accuracy with increasing training data size and different random
initializations. At the same time, we find that little of the fairness
variation is explained by model size, despite claims in the literature. To
improve model fairness without retraining, we show that two post-processing
methods developed for structured, tabular data can be successfully applied to a
range of pretrained language models. Warning: This paper contains samples of
offensive text.

positive:
We observe standard transfer learning can improve prediction accuracies of
target tasks at the cost of lowering their prediction fairness -- a phenomenon
we named discriminatory transfer. We examine prediction fairness of a standard
hypothesis transfer algorithm and a standard multi-task learning algorithm, and
show they both suffer discriminatory transfer on the real-world Communities and
Crime data set. The presented case study introduces an interaction between
fairness and transfer learning, as an extension of existing fairness studies
that focus on single task learning.

positive:
This paper investigates the application of machine learning when training a
credit decision model over real, publicly available data whilst accounting for
"bias objectives". We use the term "bias objective" to describe the requirement
that a trained model displays discriminatory bias against a given groups of
individuals that doesn't exceed a prescribed level, where such level can be
zero. This research presents an empirical study examining the tension between
competing model training objectives which in all cases include one or more bias
objectives.
  This work is motivated by the observation that the parties associated with
creditworthiness models have requirements that can not certainly be fully met
simultaneously. The research herein seeks to highlight the impracticality of
satisfying all parties' objectives, demonstrating the need for "trade-offs" to
be made. The results and conclusions presented by this paper are of particular
importance for all stakeholders within the credit scoring industry that rely
upon artificial intelligence (AI) models as part of the decision-making process
when determining the creditworthiness of individuals. This paper provides an
exposition of the difficulty of training AI models that are able to
simultaneously satisfy multiple bias objectives whilst maintaining acceptable
levels of accuracy. Stakeholders should be aware of this difficulty and should
acknowledge that some degree of discriminatory bias, across a number of
protected characteristics and formulations of bias, cannot be avoided.

positive:
We consider the problem of improving fairness when one lacks access to a
dataset labeled with protected groups, making it difficult to take advantage of
strategies that can improve fairness but require protected group labels, either
at training or runtime. To address this, we investigate improving fairness
metrics for proxy groups, and test whether doing so results in improved
fairness for the true sensitive groups. Results on benchmark and real-world
datasets demonstrate that such a proxy fairness strategy can work well in
practice. However, we caution that the effectiveness likely depends on the
choice of fairness metric, as well as how aligned the proxy groups are with the
true protected groups in terms of the constrained model parameters.

positive:
As algorithms increasingly inform and influence decisions made about
individuals, it becomes increasingly important to address concerns that these
algorithms might be discriminatory. The output of an algorithm can be
discriminatory for many reasons, most notably: (1) the data used to train the
algorithm might be biased (in various ways) to favor certain populations over
others; (2) the analysis of this training data might inadvertently or
maliciously introduce biases that are not borne out in the data. This work
focuses on the latter concern.
  We develop and study multicalbration -- a new measure of algorithmic fairness
that aims to mitigate concerns about discrimination that is introduced in the
process of learning a predictor from data. Multicalibration guarantees accurate
(calibrated) predictions for every subpopulation that can be identified within
a specified class of computations. We think of the class as being quite rich;
in particular, it can contain many overlapping subgroups of a protected group.
  We show that in many settings this strong notion of protection from
discrimination is both attainable and aligned with the goal of obtaining
accurate predictions. Along the way, we present new algorithms for learning a
multicalibrated predictor, study the computational complexity of this task, and
draw new connections to computational learning models such as agnostic
learning.

positive:
Machine learning algorithms tend to create more accurate models with the
availability of large datasets. In some cases, highly accurate models can hide
the presence of bias in the data. There are several studies published that
tackle the development of discriminatory-aware machine learning algorithms. We
center on the further evaluation of machine learning models by doing error
analysis, to understand under what conditions the model is not working as
expected. We focus on the ethical implications of avoiding error analysis, from
a falsification of results and discrimination perspective. Finally, we show
different ways to approach error analysis in non-interpretable machine learning
algorithms such as deep learning.

positive:
People are rated and ranked, towards algorithmic decision making in an
increasing number of applications, typically based on machine learning.
Research on how to incorporate fairness into such tasks has prevalently pursued
the paradigm of group fairness: giving adequate success rates to specifically
protected groups. In contrast, the alternative paradigm of individual fairness
has received relatively little attention, and this paper advances this less
explored direction. The paper introduces a method for probabilistically mapping
user records into a low-rank representation that reconciles individual fairness
and the utility of classifiers and rankings in downstream applications. Our
notion of individual fairness requires that users who are similar in all
task-relevant attributes such as job qualification, and disregarding all
potentially discriminating attributes such as gender, should have similar
outcomes. We demonstrate the versatility of our method by applying it to
classification and learning-to-rank tasks on a variety of real-world datasets.
Our experiments show substantial improvements over the best prior work for this
setting.

positive:
While Explainable Artificial Intelligence (XAI) is increasingly expanding
more areas of application, little has been applied to make deep Reinforcement
Learning (RL) more comprehensible. As RL becomes ubiquitous and used in
critical and general public applications, it is essential to develop methods
that make it better understood and more interpretable. This study proposes a
novel approach to explain cooperative strategies in multiagent RL using Shapley
values, a game theory concept used in XAI that successfully explains the
rationale behind decisions taken by Machine Learning algorithms. Through
testing common assumptions of this technique in two cooperation-centered
socially challenging multi-agent environments environments, this article argues
that Shapley values are a pertinent way to evaluate the contribution of players
in a cooperative multi-agent RL context. To palliate the high overhead of this
method, Shapley values are approximated using Monte Carlo sampling.
Experimental results on Multiagent Particle and Sequential Social Dilemmas show
that Shapley values succeed at estimating the contribution of each agent. These
results could have implications that go beyond games in economics, (e.g., for
non-discriminatory decision making, ethical and responsible AI-derived
decisions or policy making under fairness constraints). They also expose how
Shapley values only give general explanations about a model and cannot explain
a single run, episode nor justify precise actions taken by agents. Future work
should focus on addressing these critical aspects.

positive:
Machine learning models (e.g., speech recognizers) are usually trained to
minimize average loss, which results in representation disparity---minority
groups (e.g., non-native speakers) contribute less to the training objective
and thus tend to suffer higher loss. Worse, as model accuracy affects user
retention, a minority group can shrink over time. In this paper, we first show
that the status quo of empirical risk minimization (ERM) amplifies
representation disparity over time, which can even make initially fair models
unfair. To mitigate this, we develop an approach based on distributionally
robust optimization (DRO), which minimizes the worst case risk over all
distributions close to the empirical distribution. We prove that this approach
controls the risk of the minority group at each time step, in the spirit of
Rawlsian distributive justice, while remaining oblivious to the identity of the
groups. We demonstrate that DRO prevents disparity amplification on examples
where ERM fails, and show improvements in minority group user satisfaction in a
real-world text autocomplete task.

positive:
Increasingly, discrimination by algorithms is perceived as a societal and
legal problem. As a response, a number of criteria for implementing algorithmic
fairness in machine learning have been developed in the literature. This paper
proposes the Continuous Fairness Algorithm (CFA$\theta$) which enables a
continuous interpolation between different fairness definitions. More
specifically, we make three main contributions to the existing literature.
First, our approach allows the decision maker to continuously vary between
specific concepts of individual and group fairness. As a consequence, the
algorithm enables the decision maker to adopt intermediate ``worldviews'' on
the degree of discrimination encoded in algorithmic processes, adding nuance to
the extreme cases of ``we're all equal'' (WAE) and ``what you see is what you
get'' (WYSIWYG) proposed so far in the literature. Second, we use optimal
transport theory, and specifically the concept of the barycenter, to maximize
decision maker utility under the chosen fairness constraints. Third, the
algorithm is able to handle cases of intersectionality, i.e., of
multi-dimensional discrimination of certain groups on grounds of several
criteria. We discuss three main examples (credit applications; college
admissions; insurance contracts) and map out the legal and policy implications
of our approach. The explicit formalization of the trade-off between individual
and group fairness allows this post-processing approach to be tailored to
different situational contexts in which one or the other fairness criterion may
take precedence. Finally, we evaluate our model experimentally.

positive:
Data-driven learning algorithms are employed in many online applications, in
which data become available over time, like network monitoring, stock price
prediction, job applications, etc. The underlying data distribution might
evolve over time calling for model adaptation as new instances arrive and old
instances become obsolete. In such dynamic environments, the so-called data
streams, fairness-aware learning cannot be considered as a one-off requirement,
but rather it should comprise a continual requirement over the stream. Recent
fairness-aware stream classifiers ignore the problem of class imbalance, which
manifests in many real-life applications, and mitigate discrimination mainly
because they "reject" minority instances at large due to their inability to
effectively learn all classes.
  In this work, we propose \ours, an online fairness-aware approach that
maintains a valid and fair classifier over the stream. \ours~is an online
boosting approach that changes the training distribution in an online fashion
by monitoring stream's class imbalance and tweaks its decision boundary to
mitigate discriminatory outcomes over the stream. Experiments on 8 real-world
and 1 synthetic datasets from different domains with varying class imbalance
demonstrate the superiority of our method over state-of-the-art fairness-aware
stream approaches with a range (relative) increase [11.2\%-14.2\%] in balanced
accuracy, [22.6\%-31.8\%] in gmean, [42.5\%-49.6\%] in recall, [14.3\%-25.7\%]
in kappa and [89.4\%-96.6\%] in statistical parity (fairness).

positive:
In consequential domains such as recidivism prediction, facility inspection,
and benefit assignment, it's important for individuals to know the
decision-relevant information for the model's prediction. In addition,
predictions should be fair both in terms of the outcome and the justification
of the outcome. In other words, decision-relevant features should provide
sufficient information for the predicted outcome and should be independent of
the membership of individuals in protected groups such as race and gender. In
this work, we focus on the problem of (un)fairness in the justification of the
text-based neural models. We tie the explanatory power of the model to fairness
in the outcome and propose a fairness-aware summarization mechanism to detect
and counteract the bias in such models. Given a potentially biased natural
language explanation for a decision, we use a multi-task neural model and an
attribution mechanism based on integrated gradients to extract high-utility and
low-bias justifications in form of a summary. The extracted summary is then
used for training a model to make decisions for individuals. Results on several
real world datasets suggest that our method drastically limits the demographic
leakage in the input (fairness in justification) while moderately enhancing the
fairness in the outcome. Our model is also effective in detecting and
counteracting several types of data poisoning attacks that synthesize
race-coded reasoning or irrelevant justifications.

negative:
A machine learning model may exhibit discrimination when used to make
decisions involving people. One potential cause for such outcomes is that the
model uses a statistical proxy for a protected demographic attribute. In this
paper we formulate a definition of proxy use for the setting of linear
regression and present algorithms for detecting proxies. Our definition follows
recent work on proxies in classification models, and characterizes a model's
constituent behavior that: 1) correlates closely with a protected random
variable, and 2) is causally influential in the overall behavior of the model.
We show that proxies in linear regression models can be efficiently identified
by solving a second-order cone program, and further extend this result to
account for situations where the use of a certain input variable is justified
as a `business necessity'. Finally, we present empirical results on two law
enforcement datasets that exhibit varying degrees of racial disparity in
prediction outcomes, demonstrating that proxies shed useful light on the causes
of discriminatory behavior in models.

negative:
When machine-learning algorithms are deployed in high-stakes decisions, we
want to ensure that their deployment leads to fair and equitable outcomes. This
concern has motivated a fast-growing literature that focuses on diagnosing and
addressing disparities in machine predictions. However, many machine
predictions are deployed to assist in decisions where a human decision-maker
retains the ultimate decision authority. In this article, we therefore consider
how properties of machine predictions affect the resulting human decisions. We
show in a formal model that the inclusion of a biased human decision-maker can
revert common relationships between the structure of the algorithm and the
qualities of resulting decisions. Specifically, we document that excluding
information about protected groups from the prediction may fail to reduce, and
may even increase, ultimate disparities. While our concrete results rely on
specific assumptions about the data, algorithm, and decision-maker, they show
more broadly that any study of critical properties of complex decision systems,
such as the fairness of machine-assisted human decisions, should go beyond
focusing on the underlying algorithmic predictions in isolation.

negative:
The growing use of automated decision-making in critical applications, such
as crime prediction and college admission, has raised questions about fairness
in machine learning. How can we decide whether different treatments are
reasonable or discriminatory? In this paper, we investigate discrimination in
machine learning from a visual analytics perspective and propose an interactive
visualization tool, DiscriLens, to support a more comprehensive analysis. To
reveal detailed information on algorithmic discrimination, DiscriLens
identifies a collection of potentially discriminatory itemsets based on causal
modeling and classification rules mining. By combining an extended Euler
diagram with a matrix-based visualization, we develop a novel set visualization
to facilitate the exploration and interpretation of discriminatory itemsets. A
user study shows that users can interpret the visually encoded information in
DiscriLens quickly and accurately. Use cases demonstrate that DiscriLens
provides informative guidance in understanding and reducing algorithmic
discrimination.

negative:
Developing classification algorithms that are fair with respect to sensitive
attributes of the data has become an important problem due to the growing
deployment of classification algorithms in various social contexts. Several
recent works have focused on fairness with respect to a specific metric,
modeled the corresponding fair classification problem as a constrained
optimization problem, and developed tailored algorithms to solve them. Despite
this, there still remain important metrics for which we do not have fair
classifiers and many of the aforementioned algorithms do not come with
theoretical guarantees; perhaps because the resulting optimization problem is
non-convex. The main contribution of this paper is a new meta-algorithm for
classification that takes as input a large class of fairness constraints, with
respect to multiple non-disjoint sensitive attributes, and which comes with
provable guarantees. This is achieved by first developing a meta-algorithm for
a large family of classification problems with convex constraints, and then
showing that classification problems with general types of fairness constraints
can be reduced to those in this family. We present empirical results that show
that our algorithm can achieve near-perfect fairness with respect to various
fairness metrics, and that the loss in accuracy due to the imposed fairness
constraints is often small. Overall, this work unifies several prior works on
fair classification, presents a practical algorithm with theoretical
guarantees, and can handle fairness metrics that were previously not possible.

negative:
Many machine learning systems make extensive use of large amounts of data
regarding human behaviors. Several researchers have found various
discriminatory practices related to the use of human-related machine learning
systems, for example in the field of criminal justice, credit scoring and
advertising. Fair machine learning is therefore emerging as a new field of
study to mitigate biases that are inadvertently incorporated into algorithms.
Data scientists and computer engineers are making various efforts to provide
definitions of fairness. In this paper, we provide an overview of the most
widespread definitions of fairness in the field of machine learning, arguing
that the ideas highlighting each formalization are closely related to different
ideas of justice and to different interpretations of democracy embedded in our
culture. This work intends to analyze the definitions of fairness that have
been proposed to date to interpret the underlying criteria and to relate them
to different ideas of democracy.

negative:
The advent of powerful prediction algorithms led to increased automation of
high-stake decisions regarding the allocation of scarce resources such as
government spending and welfare support. This automation bears the risk of
perpetuating unwanted discrimination against vulnerable and historically
disadvantaged groups. Research on algorithmic discrimination in computer
science and other disciplines developed a plethora of fairness metrics to
detect and correct discriminatory algorithms. Drawing on robust sociological
and philosophical discourse on distributive justice, we identify the
limitations and problematic implications of prominent fairness metrics. We show
that metrics implementing equality of opportunity only apply when resource
allocations are based on deservingness, but fail when allocations should
reflect concerns about egalitarianism, sufficiency, and priority. We argue that
by cleanly distinguishing between prediction tasks and decision tasks, research
on fair machine learning could take better advantage of the rich literature on
distributive justice.

negative:
Real-world applications of machine learning tools in high-stakes domains are
often regulated to be fair, in the sense that the predicted target should
satisfy some quantitative notion of parity with respect to a protected
attribute. However, the exact tradeoff between fairness and accuracy is not
entirely clear, even for the basic paradigm of classification problems. In this
paper, we characterize an inherent tradeoff between statistical parity and
accuracy in the classification setting by providing a lower bound on the sum of
group-wise errors of any fair classifiers. Our impossibility theorem could be
interpreted as a certain uncertainty principle in fairness: if the base rates
differ among groups, then any fair classifier satisfying statistical parity has
to incur a large error on at least one of the groups. We further extend this
result to give a lower bound on the joint error of any (approximately) fair
classifiers, from the perspective of learning fair representations. To show
that our lower bound is tight, assuming oracle access to Bayes (potentially
unfair) classifiers, we also construct an algorithm that returns a randomized
classifier that is both optimal (in terms of accuracy) and fair. Interestingly,
when the protected attribute can take more than two values, an extension of
this lower bound does not admit an analytic solution. Nevertheless, in this
case, we show that the lower bound can be efficiently computed by solving a
linear program, which we term as the TV-Barycenter problem, a barycenter
problem under the TV-distance. On the upside, we prove that if the group-wise
Bayes optimal classifiers are close, then learning fair representations leads
to an alternative notion of fairness, known as the accuracy parity, which
states that the error rates are close between groups. Finally, we also conduct
experiments on real-world datasets to confirm our theoretical findings.

negative:
In this work, the problem of predicting dropout risk in undergraduate studies
is addressed from a perspective of algorithmic fairness. We develop a machine
learning method to predict the risks of university dropout and
underperformance. The objective is to understand if such a system can identify
students at risk while avoiding potential discriminatory biases. When modeling
both risks, we obtain prediction models with an Area Under the ROC Curve (AUC)
of 0.77-0.78 based on the data available at the enrollment time, before the
first year of studies starts. This data includes the students' demographics,
the high school they attended, and their admission (average) grade. Our models
are calibrated: they produce estimated probabilities for each risk, not mere
scores. We analyze if this method leads to discriminatory outcomes for some
sensitive groups in terms of prediction accuracy (AUC) and error rates
(Generalized False Positive Rate, GFPR, or Generalized False Negative Rate,
GFNR). The models exhibit some equity in terms of AUC and GFNR along groups.
The similar GFNR means a similar probability of failing to detect risk for
students who drop out. The disparities in GFPR are addressed through a
mitigation process that does not affect the calibration of the model.

negative:
As decision-making increasingly relies on Machine Learning (ML) and (big)
data, the issue of fairness in data-driven Artificial Intelligence (AI) systems
is receiving increasing attention from both research and industry. A large
variety of fairness-aware machine learning solutions have been proposed which
involve fairness-related interventions in the data, learning algorithms and/or
model outputs. However, a vital part of proposing new approaches is evaluating
them empirically on benchmark datasets that represent realistic and diverse
settings. Therefore, in this paper, we overview real-world datasets used for
fairness-aware machine learning. We focus on tabular data as the most common
data representation for fairness-aware machine learning. We start our analysis
by identifying relationships between the different attributes, particularly
w.r.t. protected attributes and class attribute, using a Bayesian network. For
a deeper understanding of bias in the datasets, we investigate the interesting
relationships using exploratory analysis.

negative:
As an important problem in modern data analytics, classification has
witnessed varieties of applications from different domains. Different from
conventional classification approaches, fair classification concerns the issues
of unintentional biases against the sensitive features (e.g., gender, race).
Due to high nonconvexity of fairness measures, existing methods are often
unable to model exact fairness, which can cause inferior fair classification
outcomes. This paper fills the gap by developing a novel unified framework to
jointly optimize accuracy and fairness. The proposed framework is versatile and
can incorporate different fairness measures studied in literature precisely as
well as can be applicable to many classifiers including deep classification
models. Specifically, in this paper, we first prove Fisher consistency of the
proposed framework. We then show that many classification models within this
framework can be recast as mixed-integer convex programs, which can be solved
effectively by off-the-shelf solvers when the instance sizes are moderate and
can be used as benchmarks to compare the efficiency of approximation
algorithms. We prove that in the proposed framework, when the classification
outcomes are known, the resulting problem, termed "unbiased subdata selection,"
is strongly polynomial-solvable and can be used to enhance the classification
fairness by selecting more representative data points. This motivates us to
develop an iterative refining strategy (IRS) to solve the large-scale
instances, where we improve the classification accuracy and conduct the
unbiased subdata selection in an alternating fashion. We study the convergence
property of IRS and derive its approximation bound. More broadly, this
framework can be leveraged to improve classification models with unbalanced
data by taking F1 score into consideration.

negative:
It is tempting to think that machines are less prone to unfairness and
prejudice. However, machine learning approaches compute their outputs based on
data. While biases can enter at any stage of the development pipeline, models
are particularly receptive to mirror biases of the datasets they are trained on
and therefore do not necessarily reflect truths about the world but, primarily,
truths about the data. To raise awareness about the relationship between modern
algorithms and the data that shape them, we use a conditional invertible neural
network to disentangle the dataset-specific information from the information
which is shared across different datasets. In this way, we can project the same
image onto different datasets, thereby revealing their inherent biases. We use
this methodology to (i) investigate the impact of dataset quality on the
performance of generative models, (ii) show how societal biases of datasets are
replicated by generative models, and (iii) present creative applications
through unpaired transfer between diverse datasets such as photographs, oil
portraits, and animes. Our code and an interactive demonstration are available
at https://github.com/CompVis/net2net .

negative:
Fairness in decision-making has been a long-standing issue in our society.
Despite the increasing number of research activities on unfairness mitigation
in machine learning models, there is little research focusing on mitigating
unfairness in human decisions. Fairness in human decisions is as important as,
if not more important than, fairness in machine learning models since there are
processes where humans make the final decisions and machine learning models can
inherit bias from the human decisions they were trained on. As a result, this
work aims to detect unfairness in human decisions, the very first step of
solving the unfair human decision problem.
  This paper proposes to utilize the existing machine learning fairness
detection mechanisms to detect unfairness in human decisions. The rationale
behind this is, while it is difficult to directly test whether a human makes
unfair decisions, with current research on machine learning fairness, it is now
easy to test, on a large scale at a low cost, whether a machine learning model
is unfair. By synthesizing unfair labels on four general machine learning
fairness datasets and one image processing dataset, this paper shows that the
proposed approach is able to detect (1) whether or not unfair labels exist in
the training data and (2) the degree and direction of the unfairness. We
believe that this work demonstrates the potential of utilizing machine learning
fairness to detect human decision fairness. Following this work, research can
be conducted on (1) preventing future unfair decisions, (2) fixing prior unfair
decisions, and (3) training a fairer machine learning model.

negative:
The causes underlying unfair decision making are complex, being internalised
in different ways by decision makers, other actors dealing with data and
models, and ultimately by the individuals being affected by these decisions.
One frequent manifestation of all these latent causes arises in the form of
missing values: protected groups are more reluctant to give information that
could be used against them, delicate information for some groups can be erased
by human operators, or data acquisition may simply be less complete and
systematic for minority groups. As a result, missing values and bias in data
are two phenomena that are tightly coupled. However, most recent techniques,
libraries and experimental results dealing with fairness in machine learning
have simply ignored missing data. In this paper, we claim that fairness
research should not miss the opportunity to deal properly with missing data. To
support this claim, (1) we analyse the sources of missing data and bias, and we
map the common causes, (2) we find that rows containing missing values are
usually fairer than the rest, which should not be treated as the uncomfortable
ugly data that different techniques and libraries get rid of at the first
occasion, and (3) we study the trade-off between performance and fairness when
the rows with missing values are used (either because the technique deals with
them directly or by imputation methods). We end the paper with a series of
recommended procedures about what to do with missing data when aiming for fair
decision making.

negative:
We present an optimization framework for learning a fair classifier in the
presence of noisy perturbations in the protected attributes. Compared to prior
work, our framework can be employed with a very general class of linear and
linear-fractional fairness constraints, can handle multiple, non-binary
protected attributes, and outputs a classifier that comes with provable
guarantees on both accuracy and fairness. Empirically, we show that our
framework can be used to attain either statistical rate or false positive rate
fairness guarantees with a minimal loss in accuracy, even when the noise is
large, in two real-world datasets.

negative:
Recent attempts to achieve fairness in predictive models focus on the balance
between fairness and accuracy. In sensitive applications such as healthcare or
criminal justice, this trade-off is often undesirable as any increase in
prediction error could have devastating consequences. In this work, we argue
that the fairness of predictions should be evaluated in context of the data,
and that unfairness induced by inadequate samples sizes or unmeasured
predictive variables should be addressed through data collection, rather than
by constraining the model. We decompose cost-based metrics of discrimination
into bias, variance, and noise, and propose actions aimed at estimating and
reducing each term. Finally, we perform case-studies on prediction of income,
mortality, and review ratings, confirming the value of this analysis. We find
that data collection is often a means to reduce discrimination without
sacrificing accuracy.

negative:
The wide spread usage of automated data-driven decision support systems has
raised a lot of concerns regarding accountability and fairness of the employed
models in the absence of human supervision. Existing fairness-aware approaches
tackle fairness as a batch learning problem and aim at learning a fair model
which can then be applied to future instances of the problem. In many
applications, however, the data comes sequentially and its characteristics
might evolve with time. In such a setting, it is counter-intuitive to "fix" a
(fair) model over the data stream as changes in the data might incur changes in
the underlying model therefore, affecting its fairness. In this work, we
propose fairness-enhancing interventions that modify the input data so that the
outcome of any stream classifier applied to that data will be fair. Experiments
on real and synthetic data show that our approach achieves good predictive
performance and low discrimination scores over the course of the stream.

negative:
Considerable research effort has been guided towards algorithmic fairness but
there is still no major breakthrough. In practice, an exhaustive search over
all possible techniques and hyperparameters is needed to find optimal
fairness-accuracy trade-offs. Hence, coupled with the lack of tools for ML
practitioners, real-world adoption of bias reduction methods is still scarce.
To address this, we present Fairband, a bandit-based fairness-aware
hyperparameter optimization (HO) algorithm. Fairband is conceptually simple,
resource-efficient, easy to implement, and agnostic to both the objective
metrics, model types and the hyperparameter space being explored. Moreover, by
introducing fairness notions into HO, we enable seamless and efficient
integration of fairness objectives into real-world ML pipelines. We compare
Fairband with popular HO methods on four real-world decision-making datasets.
We show that Fairband can efficiently navigate the fairness-accuracy trade-off
through hyperparameter optimization. Furthermore, without extra training cost,
it consistently finds configurations attaining substantially improved fairness
at a comparatively small decrease in predictive accuracy.

negative:
The newly emerged machine learning (e.g. deep learning) methods have become a
strong driving force to revolutionize a wide range of industries, such as smart
healthcare, financial technology, and surveillance systems. Meanwhile, privacy
has emerged as a big concern in this machine learning-based artificial
intelligence era. It is important to note that the problem of privacy
preservation in the context of machine learning is quite different from that in
traditional data privacy protection, as machine learning can act as both friend
and foe. Currently, the work on the preservation of privacy and machine
learning (ML) is still in an infancy stage, as most existing solutions only
focus on privacy problems during the machine learning process. Therefore, a
comprehensive study on the privacy preservation problems and machine learning
is required. This paper surveys the state of the art in privacy issues and
solutions for machine learning. The survey covers three categories of
interactions between privacy and machine learning: (i) private machine
learning, (ii) machine learning aided privacy protection, and (iii) machine
learning-based privacy attack and corresponding protection schemes. The current
research progress in each category is reviewed and the key challenges are
identified. Finally, based on our in-depth analysis of the area of privacy and
machine learning, we point out future research directions in this field.

negative:
Since many critical decisions impacting human lives are increasingly being
made by algorithms, it is important to ensure that the treatment of individuals
under such algorithms is demonstrably fair under reasonable notions of
fairness. One compelling notion proposed in the literature is that of
individual fairness (IF), which advocates that similar individuals should be
treated similarly (Dwork et al. 2012). Originally proposed for offline
decisions, this notion does not, however, account for temporal considerations
relevant for online decision-making. In this paper, we extend the notion of IF
to account for the time at which a decision is made, in settings where there
exists a notion of conduciveness of decisions as perceived by the affected
individuals. We introduce two definitions: (i) fairness-across-time (FT) and
(ii) fairness-in-hindsight (FH). FT is the simplest temporal extension of IF
where treatment of individuals is required to be individually fair relative to
the past as well as future, while in FH, we require a one-sided notion of
individual fairness that is defined relative to only the past decisions. We
show that these two definitions can have drastically different implications in
the setting where the principal needs to learn the utility model. Linear regret
relative to optimal individually fair decisions is inevitable under FT for
non-trivial examples. On the other hand, we design a new algorithm: Cautious
Fair Exploration (CaFE), which satisfies FH and achieves sub-linear regret
guarantees for a broad range of settings. We characterize lower bounds showing
that these guarantees are order-optimal in the worst case. FH can thus be
embedded as a primary safeguard against unfair discrimination in algorithmic
deployments, without hindering the ability to take good decisions in the
long-run.

negative:
For supervised learning models, the analysis of generalization ability
(generalizability) is vital because the generalizability expresses how well a
model will perform on unseen data. Traditional generalization methods, such as
the VC dimension, do not apply to deep neural network (DNN) models. Thus, new
theories to explain the generalizability of DNNs are required. In this study,
we hypothesize that the DNN with a simpler decision boundary has better
generalizability by the law of parsimony (Occam's Razor). We create the
decision boundary complexity (DBC) score to define and measure the complexity
of decision boundary of DNNs. The idea of the DBC score is to generate data
points (called adversarial examples) on or near the decision boundary. Our new
approach then measures the complexity of the boundary using the entropy of
eigenvalues of these data. The method works equally well for high-dimensional
data. We use training data and the trained model to compute the DBC score. And,
the ground truth for model's generalizability is its test accuracy. Experiments
based on the DBC score have verified our hypothesis. The DBC is shown to
provide an effective method to measure the complexity of a decision boundary
and gives a quantitative measure of the generalizability of DNNs.

negative:
Consider a binary decision making process where a single machine learning
classifier replaces a multitude of humans. We raise questions about the
resulting loss of diversity in the decision making process. We study the
potential benefits of using random classifier ensembles instead of a single
classifier in the context of fairness-aware learning and demonstrate various
attractive properties: (i) an ensemble of fair classifiers is guaranteed to be
fair, for several different measures of fairness, (ii) an ensemble of unfair
classifiers can still achieve fair outcomes, and (iii) an ensemble of
classifiers can achieve better accuracy-fairness trade-offs than a single
classifier. Finally, we introduce notions of distributional fairness to
characterize further potential benefits of random classifier ensembles.

negative:
Fairness of classification and regression has received much attention
recently and various, partially non-compatible, criteria have been proposed.
The fairness criteria can be enforced for a given classifier or, alternatively,
the data can be adapated to ensure that every classifier trained on the data
will adhere to desired fairness criteria. We present a practical data adaption
method based on quantile preservation in causal structural equation models. The
data adaptation is based on a presumed counterfactual model for the data. While
the counterfactual model itself cannot be verified experimentally, we show that
certain population notions of fairness are still guaranteed even if the
counterfactual model is misspecified. The precise nature of the fulfilled
non-causal fairness notion (such as demographic parity, separation or
sufficiency) depends on the structure of the underlying causal model and the
choice of resolving variables. We describe an implementation of the proposed
data adaptation procedure based on Random Forests and demonstrate its practical
use on simulated and real-world data.

negative:
What does it mean for an algorithm to be fair? Different papers use different
notions of algorithmic fairness, and although these appear internally
consistent, they also seem mutually incompatible. We present a mathematical
setting in which the distinctions in previous papers can be made formal. In
addition to characterizing the spaces of inputs (the "observed" space) and
outputs (the "decision" space), we introduce the notion of a construct space: a
space that captures unobservable, but meaningful variables for the prediction.
  We show that in order to prove desirable properties of the entire
decision-making process, different mechanisms for fairness require different
assumptions about the nature of the mapping from construct space to decision
space. The results in this paper imply that future treatments of algorithmic
fairness should more explicitly state assumptions about the relationship
between constructs and observations.

negative:
What does it mean for a machine learning model to be `fair', in terms which
can be operationalised? Should fairness consist of ensuring everyone has an
equal probability of obtaining some benefit, or should we aim instead to
minimise the harms to the least advantaged? Can the relevant ideal be
determined by reference to some alternative state of affairs in which a
particular social pattern of discrimination does not exist? Various definitions
proposed in recent literature make different assumptions about what terms like
discrimination and fairness mean and how they can be defined in mathematical
terms. Questions of discrimination, egalitarianism and justice are of
significant interest to moral and political philosophers, who have expended
significant efforts in formalising and defending these central concepts. It is
therefore unsurprising that attempts to formalise `fairness' in machine
learning contain echoes of these old philosophical debates. This paper draws on
existing work in moral and political philosophy in order to elucidate emerging
debates about fair machine learning.

negative:
In recent years, significant progress has been made in developing more
accurate and efficient machine learning algorithms for segmentation of medical
and natural images. In this review article, we highlight the imperative role of
machine learning algorithms in enabling efficient and accurate segmentation in
the field of medical imaging. We specifically focus on several key studies
pertaining to the application of machine learning methods to biomedical image
segmentation. We review classical machine learning algorithms such as Markov
random fields, k-means clustering, random forest, etc. Although such classical
learning models are often less accurate compared to the deep learning
techniques, they are often more sample efficient and have a less complex
structure. We also review different deep learning architectures, such as the
artificial neural networks (ANNs), the convolutional neural networks (CNNs),
and the recurrent neural networks (RNNs), and present the segmentation results
attained by those learning models that were published in the past three years.
We highlight the successes and limitations of each machine learning paradigm.
In addition, we discuss several challenges related to the training of different
machine learning models, and we present some heuristics to address those
challenges.

negative:
Deep neural networks and in particular, deep neural classifiers have become
an integral part of many modern applications. Despite their practical success,
we still have limited knowledge of how they work and the demand for such an
understanding is evergrowing. In this regard, one crucial aspect of deep neural
network classifiers that can help us deepen our knowledge about their
decision-making behavior is to investigate their decision boundaries.
Nevertheless, this is contingent upon having access to samples populating the
areas near the decision boundary. To achieve this, we propose a novel approach
we call Deep Decision boundary Instance Generation (DeepDIG). DeepDIG utilizes
a method based on adversarial example generation as an effective way of
generating samples near the decision boundary of any deep neural network model.
Then, we introduce a set of important principled characteristics that take
advantage of the generated instances near the decision boundary to provide
multifaceted understandings of deep neural networks. We have performed
extensive experiments on multiple representative datasets across various deep
neural network models and characterized their decision boundaries. The code is
publicly available at https://github.com/hamidkarimi/DeepDIG/.

negative:
Machine Learning has been successfully applied in systems applications such
as memory prefetching and caching, where learned models have been shown to
outperform heuristics. However, the lack of understanding the inner workings of
these models -- interpretability -- remains a major obstacle for adoption in
real-world deployments. Understanding a model's behavior can help system
administrators and developers gain confidence in the model, understand risks,
and debug unexpected behavior in production. Interpretability for models used
in computer systems poses a particular challenge: Unlike ML models trained on
images or text, the input domain (e.g., memory access patterns, program
counters) is not immediately interpretable. A major challenge is therefore to
explain the model in terms of concepts that are approachable to a human
practitioner. By analyzing a state-of-the-art caching model, we provide
evidence that the model has learned concepts beyond simple statistics that can
be leveraged for explanations. Our work provides a first step towards
explanability of system ML models and highlights both promises and challenges
of this emerging research area.

negative:
Fatal accidents are a major issue hindering the wide acceptance of
safety-critical systems that employ machine learning and deep learning models,
such as automated driving vehicles. In order to use machine learning in a
safety-critical system, it is necessary to demonstrate the safety and security
of the system through engineering processes. However, thus far, no such widely
accepted engineering concepts or frameworks have been established for these
systems. The key to using a machine learning model in a deductively engineered
system is decomposing the data-driven training of machine learning models into
requirement, design, and verification, particularly for machine learning models
used in safety-critical systems. Simultaneously, open problems and relevant
technical fields are not organized in a manner that enables researchers to
select a theme and work on it. In this study, we identify, classify, and
explore the open problems in engineering (safety-critical) machine learning
systems --- that is, in terms of requirement, design, and verification of
machine learning models and systems --- as well as discuss related works and
research directions, using automated driving vehicles as an example. Our
results show that machine learning models are characterized by a lack of
requirements specification, lack of design specification, lack of
interpretability, and lack of robustness. We also perform a gap analysis on a
conventional system quality standard SQuARE with the characteristics of machine
learning models to study quality models for machine learning systems. We find
that a lack of requirements specification and lack of robustness have the
greatest impact on conventional quality models.

negative:
Deep neural networks have been widely deployed in various machine learning
tasks. However, recent works have demonstrated that they are vulnerable to
adversarial examples: carefully crafted small perturbations to cause
misclassification by the network. In this work, we propose a novel defense
mechanism called Boundary Conditional GAN to enhance the robustness of deep
neural networks against adversarial examples. Boundary Conditional GAN, a
modified version of Conditional GAN, can generate boundary samples with true
labels near the decision boundary of a pre-trained classifier. These boundary
samples are fed to the pre-trained classifier as data augmentation to make the
decision boundary more robust. We empirically show that the model improved by
our approach consistently defenses against various types of adversarial attacks
successfully. Further quantitative investigations about the improvement of
robustness and visualization of decision boundaries are also provided to
justify the effectiveness of our strategy. This new defense mechanism that uses
boundary samples to enhance the robustness of networks opens up a new way to
defense adversarial attacks consistently.

negative:
We introduce a flexible family of fairness regularizers for (linear and
logistic) regression problems. These regularizers all enjoy convexity,
permitting fast optimization, and they span the rang from notions of group
fairness to strong individual fairness. By varying the weight on the fairness
regularizer, we can compute the efficient frontier of the accuracy-fairness
trade-off on any given dataset, and we measure the severity of this trade-off
via a numerical quantity we call the Price of Fairness (PoF). The centerpiece
of our results is an extensive comparative study of the PoF across six
different datasets in which fairness is a primary consideration.

negative:
Probabilistic models are a critical part of the modern deep learning toolbox
- ranging from generative models (VAEs, GANs), sequence to sequence models used
in machine translation and speech processing to models over functional spaces
(conditional neural processes, neural processes). Given the size and complexity
of these models, safely deploying them in applications requires the development
of tools to analyze their behavior rigorously and provide some guarantees that
these models are consistent with a list of desirable properties or
specifications. For example, a machine translation model should produce
semantically equivalent outputs for innocuous changes in the input to the
model. A functional regression model that is learning a distribution over
monotonic functions should predict a larger value at a larger input.
Verification of these properties requires a new framework that goes beyond
notions of verification studied in deterministic feedforward networks, since
requiring worst-case guarantees in probabilistic models is likely to produce
conservative or vacuous results. We propose a novel formulation of verification
for deep probabilistic models that take in conditioning inputs and sample
latent variables in the course of producing an output: We require that the
output of the model satisfies a linear constraint with high probability over
the sampling of latent variables and for every choice of conditioning input to
the model. We show that rigorous lower bounds on the probability that the
constraint is satisfied can be obtained efficiently. Experiments with neural
processes show that several properties of interest while modeling functional
spaces can be modeled within this framework (monotonicity, convexity) and
verified efficiently using our algorithms

negative:
Many recent works on knowledge distillation have provided ways to transfer
the knowledge of a trained network for improving the learning process of a new
one, but finding a good technique for knowledge distillation is still an open
problem. In this paper, we provide a new perspective based on a decision
boundary, which is one of the most important component of a classifier. The
generalization performance of a classifier is closely related to the adequacy
of its decision boundary, so a good classifier bears a good decision boundary.
Therefore, transferring information closely related to the decision boundary
can be a good attempt for knowledge distillation. To realize this goal, we
utilize an adversarial attack to discover samples supporting a decision
boundary. Based on this idea, to transfer more accurate information about the
decision boundary, the proposed algorithm trains a student classifier based on
the adversarial samples supporting the decision boundary. Experiments show that
the proposed method indeed improves knowledge distillation and achieves the
state-of-the-arts performance.

negative:
Interpretable machine learning tackles the important problem that humans
cannot understand the behaviors of complex machine learning models and how
these models arrive at a particular decision. Although many approaches have
been proposed, a comprehensive understanding of the achievements and challenges
is still lacking. We provide a survey covering existing techniques to increase
the interpretability of machine learning models. We also discuss crucial issues
that the community should consider in future work such as designing
user-friendly explanations and developing comprehensive evaluation metrics to
further push forward the area of interpretable machine learning.

negative:
Due to the increasing use of machine learning in practice it becomes more and
more important to be able to explain the prediction and behavior of machine
learning models. An instance of explanations are counterfactual explanations
which provide an intuitive and useful explanations of machine learning models.
In this survey we review model-specific methods for efficiently computing
counterfactual explanations of many different machine learning models and
propose methods for models that have not been considered in literature so far.

negative:
While machine learning is traditionally a resource intensive task, embedded
systems, autonomous navigation and the vision of the Internet-of-Things fuel
the interest in resource efficient approaches. These approaches require a
carefully chosen trade-off between performance and resource consumption in
terms of computation and energy. On top of this, it is crucial to treat
uncertainty in a consistent manner in all but the simplest applications of
machine learning systems. In particular, a desideratum for any real-world
system is to be robust in the presence of outliers and corrupted data, as well
as being `aware' of its limits, i.e.\ the system should maintain and provide an
uncertainty estimate over its own predictions. These complex demands are among
the major challenges in current machine learning research and key to ensure a
smooth transition of machine learning technology into every day's applications.
In this article, we provide an overview of the current state of the art of
machine learning techniques facilitating these real-world requirements. First
we provide a comprehensive review of resource-efficiency in deep neural
networks with focus on techniques for model size reduction, compression and
reduced precision. These techniques can be applied during training or as
post-processing and are widely used to reduce both computational complexity and
memory footprint. As most (practical) neural networks are limited in their ways
to treat uncertainty, we contrast them with probabilistic graphical models,
which readily serve these desiderata by means of probabilistic inference. In
that way, we provide an extensive overview of the current state-of-the-art of
robust and efficient machine learning for real-world systems.

negative:
Machine learning is a modern approach to problem-solving and task automation.
In particular, machine learning is concerned with the development and
applications of algorithms that can recognize patterns in data and use them for
predictive modeling. Artificial neural networks are a particular class of
machine learning algorithms and models that evolved into what is now described
as deep learning. Given the computational advances made in the last decade,
deep learning can now be applied to massive data sets and in innumerable
contexts. Therefore, deep learning has become its own subfield of machine
learning. In the context of biological research, it has been increasingly used
to derive novel insights from high-dimensional biological data. To make the
biological applications of deep learning more accessible to scientists who have
some experience with machine learning, we solicited input from a community of
researchers with varied biological and deep learning interests. These
individuals collaboratively contributed to this manuscript's writing using the
GitHub version control platform and the Manubot manuscript generation toolset.
The goal was to articulate a practical, accessible, and concise set of
guidelines and suggestions to follow when using deep learning. In the course of
our discussions, several themes became clear: the importance of understanding
and applying machine learning fundamentals as a baseline for utilizing deep
learning, the necessity for extensive model comparisons with careful
evaluation, and the need for critical thought in interpreting results generated
by deep learning, among others.

negative:
Explainable Artificial Intelligence (or xAI) has become an important research
topic in the fields of Machine Learning and Deep Learning. In this paper, we
propose a Genetic Programming (GP) based approach, named Genetic Programming
Explainer (GPX), to the problem of explaining decisions computed by AI systems.
The method generates a noise set located in the neighborhood of the point of
interest, whose prediction should be explained, and fits a local explanation
model for the analyzed sample. The tree structure generated by GPX provides a
comprehensible analytical, possibly non-linear, symbolic expression which
reflects the local behavior of the complex model. We considered three machine
learning techniques that can be recognized as complex black-box models: Random
Forest, Deep Neural Network and Support Vector Machine in twenty data sets for
regression and classifications problems. Our results indicate that the GPX is
able to produce more accurate understanding of complex models than the state of
the art. The results validate the proposed approach as a novel way to deploy GP
to improve interpretability.

negative:
Many works demonstrate that deep learning system is vulnerable to adversarial
attack. A deep learning system consists of two parts: the deep learning task
and the deep model. Nowadays, most existing works investigate the impact of the
deep model on robustness of deep learning systems, ignoring the impact of the
learning task. In this paper, we adopt the binary and interval label encoding
strategy to redefine the classification task and design corresponding loss to
improve robustness of the deep learning system. Our method can be viewed as
improving the robustness of deep learning systems from both the learning task
and deep model. Experimental results demonstrate that our learning task-aware
method is much more robust than traditional classification while retaining the
accuracy.

negative:
Research in machine learning is at a turning point. While supervised deep
learning has conquered the field at a breathtaking pace and demonstrated the
ability to solve inference problems with unprecedented accuracy, it still does
not quite live up to its name if we think of learning as the process of
acquiring knowledge about a subject or problem. Major weaknesses of present-day
deep learning models are, for instance, their lack of adaptability to changes
of environment or their incapability to perform other kinds of tasks than the
one they were trained for. While it is still unclear how to overcome these
limitations, one can observe a paradigm shift within the machine learning
community, with research interests shifting away from increasing the
performance of highly parameterized models to exceedingly specific tasks, and
towards employing machine learning algorithms in highly diverse domains. This
research question can be approached from different angles. For instance, the
field of Informed AI investigates the problem of infusing domain knowledge into
a machine learning model, by using techniques such as regularization, data
augmentation or post-processing.
  On the other hand, a remarkable number of works in the recent years has
focused on developing models that by themselves guarantee a certain degree of
versatility and invariance with respect to the domain or problem at hand. Thus,
rather than investigating how to provide domain-specific knowledge to machine
learning models, these works explore methods that equip the models with the
capability of acquiring the knowledge by themselves. This white paper provides
an introduction and discussion of this emerging field in machine learning
research. To this end, it reviews the role of knowledge in machine learning,
and discusses its relation to the concept of invariance, before providing a
literature review of the field.

negative:
Machine learning models always make a prediction, even when it is likely to
be inaccurate. This behavior should be avoided in many decision support
applications, where mistakes can have severe consequences. Albeit already
studied in 1970, machine learning with a reject option recently gained
interest. This machine learning subfield enables machine learning models to
abstain from making a prediction when likely to make a mistake.
  This survey aims to provide an overview on machine learning with a reject
option. We introduce the conditions leading to two types of rejection,
ambiguity and novelty rejection. Moreover, we define the existing architectures
for models with a reject option, describe the standard learning strategies to
train such models and relate traditional machine learning techniques to
rejection. Additionally, we review strategies to evaluate a model's predictive
and rejective quality. Finally, we provide examples of relevant application
domains and show how machine learning with rejection relates to other machine
learning research areas.

negative:
Deep learning has achieved a great success in many areas, from computer
vision to natural language processing, to game playing, and much more. Yet,
what deep learning is really doing is still an open question. There are a lot
of works in this direction. For example, [5] tried to explain deep learning by
group renormalization, and [6] tried to explain deep learning from the view of
functional approximation. In order to address this very crucial question, here
we see deep learning from perspective of mechanical learning and learning
machine (see [1], [2]). From this particular angle, we can see deep learning
much better and answer with confidence: What deep learning is really doing? why
it works well, how it works, and how much data is necessary for learning. We
also will discuss advantages and disadvantages of deep learning at the end of
this work.

negative:
Various health-care applications such as assisted living, fall detection
etc., require modeling of user behavior through Human Activity Recognition
(HAR). HAR using mobile- and wearable-based deep learning algorithms have been
on the rise owing to the advancements in pervasive computing. However, there
are two other challenges that need to be addressed: first, the deep learning
model should support on-device incremental training (model updation) from
real-time incoming data points to learn user behavior over time, while also
being resource-friendly; second, a suitable ground truthing technique (like
Active Learning) should help establish labels on-the-fly while also selecting
only the most informative data points to query from an oracle. Hence, in this
paper, we propose ActiveHARNet, a resource-efficient deep ensembled model which
supports on-device Incremental Learning and inference, with capabilities to
represent model uncertainties through approximations in Bayesian Neural
Networks using dropout. This is combined with suitable acquisition functions
for active learning. Empirical results on two publicly available wrist-worn HAR
and fall detection datasets indicate that ActiveHARNet achieves considerable
efficiency boost during inference across different users, with a substantially
low number of acquired pool points (at least 60% reduction) during incremental
learning on both datasets experimented with various acquisition functions, thus
demonstrating deployment and Incremental Learning feasibility.

negative:
Machine learning models are widely used for real-world applications, such as
document analysis and vision. Constrained machine learning problems are
problems where learned models have to both be accurate and respect constraints.
For continuous convex constraints, many works have been proposed, but learning
under combinatorial constraints is still a hard problem. The goal of this paper
is to broaden the modeling capacity of constrained machine learning problems by
incorporating existing work from combinatorial optimization. We propose first a
general framework called BaGeL (Branch, Generate and Learn) which applies
Branch and Bound to constrained learning problems where a learning problem is
generated and trained at each node until only valid models are obtained.
Because machine learning has specific requirements, we also propose an extended
table constraint to split the space of hypotheses. We validate the approach on
two examples: a linear regression under configuration constraints and a
non-negative matrix factorization with prior knowledge for latent semantics
analysis.

negative:
Recent success of machine learning in many domains has been overwhelming,
which often leads to false expectations regarding the capabilities of behavior
learning in robotics. In this survey, we analyze the current state of machine
learning for robotic behaviors. We will give a broad overview of behaviors that
have been learned and used on real robots. Our focus is on kinematically or
sensorially complex robots. That includes humanoid robots or parts of humanoid
robots, for example, legged robots or robotic arms. We will classify presented
behaviors according to various categories and we will draw conclusions about
what can be learned and what should be learned. Furthermore, we will give an
outlook on problems that are challenging today but might be solved by machine
learning in the future and argue that classical robotics and other approaches
from artificial intelligence should be integrated more with machine learning to
form complete, autonomous systems.

negative:
Overfitting is a major problem in training machine learning models,
specifically deep neural networks. This problem may be caused by imbalanced
datasets and initialization of the model parameters, which conforms the model
too closely to the training data and negatively affects the generalization
performance of the model for unseen data. The original dropout is a
regularization technique to drop hidden units randomly during training. In this
paper, we propose an adaptive technique to wisely drop the visible and hidden
units in a deep neural network using Ising energy of the network. The
preliminary results show that the proposed approach can keep the classification
performance competitive to the original network while eliminating optimization
of unnecessary network parameters in each training cycle. The dropout state of
units can also be applied to the trained (inference) model. This technique
could compress the network in terms of number of parameters up to 41.18% and
55.86% for the classification task on the MNIST and Fashion-MNIST datasets,
respectively.

negative:
Many end-user evaluations of data visualization techniques have been run
during the last decades. Their results are cornerstones to build efficient
visualization systems. However, designing such an evaluation is always complex
and time-consuming and may end in a lack of statistical evidence and
reproducibility. We believe that modern and efficient computer vision
techniques, such as deep convolutional neural networks (CNNs), may help
visualization researchers to build and/or adjust their evaluation hypothesis.
The basis of our idea is to train machine learning models on several
visualization techniques to solve a specific task. Our assumption is that it is
possible to compare the efficiency of visualization techniques based on the
performance of their corresponding model. As current machine learning models
are not able to strictly reflect human capabilities, including their
imperfections, such results should be interpreted with caution. However, we
think that using machine learning-based pre-evaluation, as a pre-process of
standard user evaluations, should help researchers to perform a more exhaustive
study of their design space. Thus, it should improve their final user
evaluation by providing it better test cases. In this paper, we present the
results of two experiments we have conducted to assess how correlated the
performance of users and computer vision techniques can be. That study compares
two mainstream graph visualization techniques: node-link (\NL) and
adjacency-matrix (\MD) diagrams. Using two well-known deep convolutional neural
networks, we partially reproduced user evaluations from Ghoniem \textit{et al.}
and from Okoe \textit{et al.}. These experiments showed that some user
evaluation results can be reproduced automatically.

negative:
We survey in this article the connections between Machine Learning and
Control Theory. Control Theory provide useful concepts and tools for Machine
Learning. Conversely Machine Learning can be used to solve large control
problems. In the first part of the paper, we develop the connections between
reinforcement learning and Markov Decision Processes, which are discrete time
control problems. In the second part, we review the concept of supervised
learning and the relation with static optimization. Deep learning which extends
supervised learning, can be viewed as a control problem. In the third part, we
present the links between stochastic gradient descent and mean-field theory.
Conversely, in the fourth and fifth parts, we review machine learning
approaches to stochastic control problems, and focus on the deterministic case,
to explain, more easily, the numerical algorithms.

negative:
The increasing incorporation of Artificial Intelligence in the form of
automated systems into decision-making procedures highlights not only the
importance of decision theory for automated systems but also the need for these
decision procedures to be explainable to the people involved in them.
Traditional realist accounts of explanation, wherein explanation is a relation
that holds (or does not hold) eternally between an explanans and an
explanandum, are not adequate to account for the notion of explanation required
for artificial decision procedures. We offer an alternative account of
explanation as used in the context of automated decision-making that makes
explanation an epistemic phenomenon, and one that is dependent on context. This
account of explanation better accounts for the way that we talk about, and use,
explanations and derived concepts, such as `explanatory power', and also allows
us to differentiate between reasons or causes on the one hand, which do not
need to have an epistemic aspect, and explanations on the other, which do have
such an aspect. Against this theoretical backdrop we then review existing
approaches to explanation in Artificial Intelligence and Machine Learning, and
suggest desiderata which truly explainable decision systems should fulfill.

negative:
The additive margin softmax (AM-Softmax) loss has delivered remarkable
performance in speaker verification. A supposed behavior of AM-Softmax is that
it can shrink within-class variation by putting emphasis on target logits,
which in turn improves margin between target and non-target classes. In this
paper, we conduct a careful analysis on the behavior of AM-Softmax loss, and
show that this loss does not implement real max-margin training. Based on this
observation, we present a Real AM-Softmax loss which involves a true margin
function in the softmax training. Experiments conducted on VoxCeleb1, SITW and
CNCeleb demonstrated that the corrected AM-Softmax loss consistently
outperforms the original one. The code has been released at
https://gitlab.com/csltstu/sunine.

negative:
Breakthroughs in machine learning are rapidly changing science and society,
yet our fundamental understanding of this technology has lagged far behind.
Indeed, one of the central tenets of the field, the bias-variance trade-off,
appears to be at odds with the observed behavior of methods used in the modern
machine learning practice. The bias-variance trade-off implies that a model
should balance under-fitting and over-fitting: rich enough to express
underlying structure in data, simple enough to avoid fitting spurious patterns.
However, in the modern practice, very rich models such as neural networks are
trained to exactly fit (i.e., interpolate) the data. Classically, such models
would be considered over-fit, and yet they often obtain high accuracy on test
data. This apparent contradiction has raised questions about the mathematical
foundations of machine learning and their relevance to practitioners.
  In this paper, we reconcile the classical understanding and the modern
practice within a unified performance curve. This "double descent" curve
subsumes the textbook U-shaped bias-variance trade-off curve by showing how
increasing model capacity beyond the point of interpolation results in improved
performance. We provide evidence for the existence and ubiquity of double
descent for a wide spectrum of models and datasets, and we posit a mechanism
for its emergence. This connection between the performance and the structure of
machine learning models delineates the limits of classical analyses, and has
implications for both the theory and practice of machine learning.

negative:
Class labels have been empirically shown useful in improving the sample
quality of generative adversarial nets (GANs). In this paper, we mathematically
study the properties of the current variants of GANs that make use of class
label information. With class aware gradient and cross-entropy decomposition,
we reveal how class labels and associated losses influence GAN's training.
Based on that, we propose Activation Maximization Generative Adversarial
Networks (AM-GAN) as an advanced solution. Comprehensive experiments have been
conducted to validate our analysis and evaluate the effectiveness of our
solution, where AM-GAN outperforms other strong baselines and achieves
state-of-the-art Inception Score (8.91) on CIFAR-10. In addition, we
demonstrate that, with the Inception ImageNet classifier, Inception Score
mainly tracks the diversity of the generator, and there is, however, no
reliable evidence that it can reflect the true sample quality. We thus propose
a new metric, called AM Score, to provide a more accurate estimation of the
sample quality. Our proposed model also outperforms the baseline methods in the
new metric.

negative:
We present an approach to analyze $C^1(\mathbb{R}^m)$ functions that
addresses limitations present in the Active Subspaces (AS) method of
Constantine et al.(2015; 2014). Under appropriate hypotheses, our Active
Manifolds (AM) method identifies a 1-D curve in the domain (the active
manifold) on which nearly all values of the unknown function are attained, and
which can be exploited for approximation or analysis, especially when $m$ is
large (high-dimensional input space). We provide theorems justifying our AM
technique and an algorithm permitting functional approximation and sensitivity
analysis. Using accessible, low-dimensional functions as initial examples, we
show AM reduces approximation error by an order of magnitude compared to AS, at
the expense of more computation. Following this, we revisit the sensitivity
analysis by Glaws et al. (2017), who apply AS to analyze a magnetohydrodynamic
power generator model, and compare the performance of AM on the same data. Our
analysis provides detailed information not captured by AS, exhibiting the
influence of each parameter individually along an active manifold. Overall, AM
represents a novel technique for analyzing functional models with benefits
including: reducing $m$-dimensional analysis to a 1-D analogue, permitting more
accurate regression than AS (at more computational expense), enabling more
informative sensitivity analysis, and granting accessible visualizations(2-D
plots) of parameter sensitivity along the AM.

negative:
The key to success in machine learning (ML) is the use of effective data
representations. Traditionally, data representations were hand-crafted.
Recently it has been demonstrated that, given sufficient data, deep neural
networks can learn effective implicit representations from simple input
representations. However, for most scientific problems, the use of deep
learning is not appropriate as the amount of available data is limited, and/or
the output models must be explainable. Nevertheless, many scientific problems
do have significant amounts of data available on related tasks, which makes
them amenable to multi-task learning, i.e. learning many related problems
simultaneously. Here we propose a novel and general representation learning
approach for multi-task learning that works successfully with small amounts of
data. The fundamental new idea is to transform an input intrinsic data
representation (i.e., handcrafted features), to an extrinsic representation
based on what a pre-trained set of models predict about the examples. This
transformation has the dual advantages of producing significantly more accurate
predictions, and providing explainable models. To demonstrate the utility of
this transformative learning approach, we have applied it to three real-world
scientific problems: drug-design (quantitative structure activity relationship
learning), predicting human gene expression (across different tissue types and
drug treatments), and meta-learning for machine learning (predicting which
machine learning methods work best for a given problem). In all three problems,
transformative machine learning significantly outperforms the best intrinsic
representation.

negative:
This paper surveys the machine learning literature and presents in an
optimization framework several commonly used machine learning approaches.
Particularly, mathematical optimization models are presented for regression,
classification, clustering, deep learning, and adversarial learning, as well as
new emerging applications in machine teaching, empirical model learning, and
Bayesian network structure learning. Such models can benefit from the
advancement of numerical optimization techniques which have already played a
distinctive role in several machine learning settings. The strengths and the
shortcomings of these models are discussed and potential research directions
and open problems are highlighted.

negative:
In realistic environments, speech is usually interfered by various noise and
reverberation, which dramatically degrades the performance of automatic speech
recognition (ASR) systems. To alleviate this issue, the commonest way is to use
a well-designed speech enhancement approach as the front-end of ASR. However,
more complex pipelines, more computations and even higher hardware costs
(microphone array) are additionally consumed for this kind of methods. In
addition, speech enhancement would result in speech distortions and mismatches
to training. In this paper, we propose an adversarial training method to
directly boost noise robustness of acoustic model. Specifically, a jointly
compositional scheme of generative adversarial net (GAN) and neural
network-based acoustic model (AM) is used in the training phase. GAN is used to
generate clean feature representations from noisy features by the guidance of a
discriminator that tries to distinguish between the true clean signals and
generated signals. The joint optimization of generator, discriminator and AM
concentrates the strengths of both GAN and AM for speech recognition.
Systematic experiments on CHiME-4 show that the proposed method significantly
improves the noise robustness of AM and achieves the average relative error
rate reduction of 23.38% and 11.54% on the development and test set,
respectively.

negative:
Deep Learning is one of the newest trends in Machine Learning and Artificial
Intelligence research. It is also one of the most popular scientific research
trends now-a-days. Deep learning methods have brought revolutionary advances in
computer vision and machine learning. Every now and then, new and new deep
learning techniques are being born, outperforming state-of-the-art machine
learning and even existing deep learning techniques. In recent years, the world
has seen many major breakthroughs in this field. Since deep learning is
evolving at a huge speed, its kind of hard to keep track of the regular
advances especially for new researchers. In this paper, we are going to briefly
discuss about recent advances in Deep Learning for past few years.

negative:
Deep learning has emerged as a powerful machine learning technique that
learns multiple layers of representations or features of the data and produces
state-of-the-art prediction results. Along with the success of deep learning in
many other application domains, deep learning is also popularly used in
sentiment analysis in recent years. This paper first gives an overview of deep
learning and then provides a comprehensive survey of its current applications
in sentiment analysis.

negative:
The recent explosion of machine learning (ML) and artificial intelligence
(AI) shows great potential in the breakthrough of metal additive manufacturing
(AM) process modeling. However, the success of conventional machine learning
tools in data science is primarily attributed to the unprecedented large amount
of labeled data-sets (big data), which can be either obtained by experiments or
first-principle simulations. Unfortunately, these labeled data-sets are
expensive to obtain in AM due to the high expense of the AM experiments and
prohibitive computational cost of high-fidelity simulations.
  We propose a physics-informed neural network (PINN) framework that fuses both
data and first physical principles, including conservation laws of momentum,
mass, and energy, into the neural network to inform the learning processes. To
the best knowledge of the authors, this is the first application of PINN to
three dimensional AM processes modeling. Besides, we propose a hard-type
approach for Dirichlet boundary conditions (BCs) based on a Heaviside function,
which can not only enforce the BCs but also accelerate the learning process.
The PINN framework is applied to two representative metal manufacturing
problems, including the 2018 NIST AM-Benchmark test series. We carefully assess
the performance of the PINN model by comparing the predictions with available
experimental data and high-fidelity simulation results. The investigations show
that the PINN, owed to the additional physical knowledge, can accurately
predict the temperature and melt pool dynamics during metal AM processes with
only a moderate amount of labeled data-sets. The foray of PINN to metal AM
shows the great potential of physics-informed deep learning for broader
applications to advanced manufacturing.

negative:
The great success of deep learning shows that its technology contains
profound truth, and understanding its internal mechanism not only has important
implications for the development of its technology and effective application in
various fields, but also provides meaningful insights into the understanding of
human brain mechanism. At present, most of the theoretical research on deep
learning is based on mathematics. This dissertation proposes that the neural
network of deep learning is a physical system, examines deep learning from
three different perspectives: microscopic, macroscopic, and physical world
views, answers multiple theoretical puzzles in deep learning by using physics
principles. For example, from the perspective of quantum mechanics and
statistical physics, this dissertation presents the calculation methods for
convolution calculation, pooling, normalization, and Restricted Boltzmann
Machine, as well as the selection of cost functions, explains why deep learning
must be deep, what characteristics are learned in deep learning, why
Convolutional Neural Networks do not have to be trained layer by layer, and the
limitations of deep learning, etc., and proposes the theoretical direction and
basis for the further development of deep learning now and in the future. The
brilliance of physics flashes in deep learning, we try to establish the deep
learning technology based on the scientific theory of physics.

negative:
Machine learning has made tremendous progress in recent years and received
large amounts of public attention. Though we are still far from designing a
full artificially intelligent agent, machine learning has brought us many
applications in which computers solve human learning tasks remarkably well.
Much of this progress comes from a recent trend within machine learning, called
deep learning. Deep learning models are responsible for many state-of-the-art
applications of machine learning. Despite their success, deep learning models
are hard to train, very difficult to understand, and often times so complex
that training is only possible on very large GPU clusters. Lots of work has
been done on enabling neural networks to learn efficiently. However, the design
and architecture of such neural networks is often done manually through trial
and error and expert knowledge. This thesis inspects different approaches,
existing and novel, to automate the design of deep feedforward neural networks
in an attempt to create less complex models with good performance that take
away the burden of deciding on an architecture and make it more efficient to
design and train such deep networks.

negative:
Machine learning is poised as a very powerful tool that can drastically
improve our ability to carry out scientific research. However, many issues need
to be addressed before this becomes a reality. This article focuses on one
particular issue of broad interest: How can we integrate machine learning with
physics-based modeling to develop new interpretable and truly reliable physical
models? After introducing the general guidelines, we discuss the two most
important issues for developing machine learning-based physical models:
Imposing physical constraints and obtaining optimal datasets. We also provide a
simple and intuitive explanation for the fundamental reasons behind the success
of modern machine learning, as well as an introduction to the concurrent
machine learning framework needed for integrating machine learning with
physics-based modeling. Molecular dynamics and moment closure of kinetic
equations are used as examples to illustrate the main issues discussed. We end
with a general discussion on where this integration will lead us to, and where
the new frontier will be after machine learning is successfully integrated into
scientific modeling.

negative:
Additive Manufacturing (AM) is a crucial component of the smart industry. In
this paper, we propose an automated quality grading system for the AM process
using a deep convolutional neural network (CNN) model. The CNN model is trained
offline using the images of the internal and surface defects in the
layer-by-layer deposition of materials and tested online by studying the
performance of detecting and classifying the failure in AM process at different
extruder speeds and temperatures. The model demonstrates the accuracy of 94%
and specificity of 96%, as well as above 75% in three classifier measures of
the Fscore, the sensitivity, and precision for classifying the quality of the
printing process in five grades in real-time. The proposed online model adds an
automated, consistent, and non-contact quality control signal to the AM process
that eliminates the manual inspection of parts after they are entirely built.
The quality monitoring signal can also be used by the machine to suggest
remedial actions by adjusting the parameters in real-time. The proposed quality
predictive model serves as a proof-of-concept for any type of AM machines to
produce reliable parts with fewer quality hiccups while limiting the waste of
both time and materials.

negative:
Speech-driven visual speech synthesis involves mapping features extracted
from acoustic speech to the corresponding lip animation controls for a face
model. This mapping can take many forms, but a powerful approach is to use deep
neural networks (DNNs). However, a limitation is the lack of synchronized
audio, video, and depth data required to reliably train the DNNs, especially
for speaker-independent models. In this paper, we investigate adapting an
automatic speech recognition (ASR) acoustic model (AM) for the visual speech
synthesis problem. We train the AM on ten thousand hours of audio-only data.
The AM is then adapted to the visual speech synthesis domain using ninety hours
of synchronized audio-visual speech. Using a subjective assessment test, we
compared the performance of the AM-initialized DNN to one with a random
initialization. The results show that viewers significantly prefer animations
generated from the AM-initialized DNN than the ones generated using the
randomly initialized model. We conclude that visual speech synthesis can
significantly benefit from the powerful representation of speech in the ASR
acoustic models.

negative:
Machine learning is usually defined in behaviourist terms, where external
validation is the primary mechanism of learning. In this paper, I argue for a
more holistic interpretation in which finding more probable, efficient and
abstract representations is as central to learning as performance. In other
words, machine learning should be extended with strategies to reason over its
own learning process, leading to so-called meta-cognitive machine learning. As
such, the de facto definition of machine learning should be reformulated in
these intrinsically multi-objective terms, taking into account not only the
task performance but also internal learning objectives. To this end, we suggest
a "model entropy function" to be defined that quantifies the efficiency of the
internal learning processes. It is conjured that the minimization of this model
entropy leads to concept formation. Besides philosophical aspects, some initial
illustrations are included to support the claims.

negative:
The paper examines the potential of deep learning to support decisions in
financial risk management. We develop a deep learning model for predicting
whether individual spread traders secure profits from future trades. This task
embodies typical modeling challenges faced in risk and behavior forecasting.
Conventional machine learning requires data that is representative of the
feature-target relationship and relies on the often costly development,
maintenance, and revision of handcrafted features. Consequently, modeling
highly variable, heterogeneous patterns such as trader behavior is challenging.
Deep learning promises a remedy. Learning hierarchical distributed
representations of the data in an automatic manner (e.g. risk taking behavior),
it uncovers generative features that determine the target (e.g., trader's
profitability), avoids manual feature engineering, and is more robust toward
change (e.g. dynamic market conditions). The results of employing a deep
network for operational risk forecasting confirm the feature learning
capability of deep learning, provide guidance on designing a suitable network
architecture and demonstrate the superiority of deep learning over machine
learning and rule-based benchmarks.

negative:
Many important NLP problems can be posed as dual-sequence or
sequence-to-sequence modeling tasks. Recent advances in building end-to-end
neural architectures have been highly successful in solving such tasks. In this
work we propose a new architecture for dual-sequence modeling that is based on
associative memory. We derive AM-RNNs, a recurrent associative memory (AM)
which augments generic recurrent neural networks (RNN). This architecture is
extended to the Dual AM-RNN which operates on two AMs at once. Our models
achieve very competitive results on textual entailment. A qualitative analysis
demonstrates that long range dependencies between source and target-sequence
can be bridged effectively using Dual AM-RNNs. However, an initial experiment
on auto-encoding reveals that these benefits are not exploited by the system
when learning to solve sequence-to-sequence tasks which indicates that
additional supervision or regularization is needed.

negative:
Affiliate Marketing (AM) has become an important and cost effective tool for
e-commerce. There are numerous risks and vulnerabilities that are typically
associated with AM. Though a well-planned AM model can greatly benefit the
e-commerce strategies of an enterprise, a haphazardly implemented system can
expose a business enterprise to major risks and vulnerabilities, which can lead
to great financial losses through fraudulent activities. This
research-in-progress has identified some of the risks and the technical
background of those scenarios. The research will now move on to build a
functional prototype of an AM network to design and test solutions to control
the identified risks.

negative:
Medical terminology normalization aims to map the clinical mention to
terminologies come from a knowledge base, which plays an important role in
analyzing Electronic Health Record(EHR) and many downstream tasks. In this
paper, we focus on Chinese procedure terminology normalization. The expression
of terminologies are various and one medical mention may be linked to multiple
terminologies. Previous study explores some methods such as multi-class
classification or learning to rank(LTR) to sort the terminologies by literature
and semantic information. However, these information is inadequate to find the
right terminologies, particularly in multi-implication cases. In this work, we
propose a combined recall and rank framework to solve the above problems. This
framework is composed of a multi-task candidate generator(MTCG), a keywords
attentive ranker(KAR) and a fusion block(FB). MTCG is utilized to predict the
mention implication number and recall candidates with semantic similarity. KAR
is based on Bert with a keywords attentive mechanism which focuses on keywords
such as procedure sites and procedure types. FB merges the similarity come from
MTCG and KAR to sort the terminologies from different perspectives. Detailed
experimental analysis shows our proposed framework has a remarkable improvement
on both performance and efficiency.

negative:
A neuroscience method to understanding the brain is to find and study the
preferred stimuli that highly activate an individual cell or groups of cells.
Recent advances in machine learning enable a family of methods to synthesize
preferred stimuli that cause a neuron in an artificial or biological brain to
fire strongly. Those methods are known as Activation Maximization (AM) or
Feature Visualization via Optimization. In this chapter, we (1) review existing
AM techniques in the literature; (2) discuss a probabilistic interpretation for
AM; and (3) review the applications of AM in debugging and explaining networks.

negative:
The ability to accurately quantify the performance an additively manufactured
(AM) product is important for a widespread industry adoption of AM as the
design is required to: (1) satisfy geometrical constraints, (2) satisfy
structural constraints dictated by its intended function, and (3) be cost
effective compared to traditional manufacturing methods. Optimization
techniques offer design aids in creating cost-effective structures that meet
the prescribed structural objectives. The fundamental problem in existing
approaches lies in the difficulty to quantify the structural performance as
each unique design leads to a new set of analyses to determine the structural
robustness and such analyses can be very costly due to the complexity of in-use
forces experienced by the structure. This work develops computationally
tractable methods tailored to maximize the structural performance of AM
products. A geometry preserving build orientation optimization method as well
as data-driven shape optimization approaches to structural design are
presented. Proposed methods greatly enhance the value of AM technology by
taking advantage of the design space enabled by it for a broad class of
problems involving complex in-use loads.

negative:
Programming by example is the problem of synthesizing a program from a small
set of input / output pairs. Recent works applying machine learning methods to
this task show promise, but are typically reliant on generating synthetic
examples for training. A particular challenge lies in generating meaningful
sets of inputs and outputs, which well-characterize a given program and
accurately demonstrate its behavior. Where examples used for testing are
generated by the same method as training data then the performance of a model
may be partly reliant on this similarity. In this paper we introduce a novel
approach using an SMT solver to synthesize inputs which cover a diverse set of
behaviors for a given program. We carry out a case study comparing this method
to existing synthetic data generation procedures in the literature, and find
that data generated using our approach improves both the discriminatory power
of example sets and the ability of trained machine learning models to
generalize to unfamiliar data.

negative:
The study of unsupervised learning can be generally divided into two
categories: imitation learning and reinforcement learning. In imitation
learning the machine learns by mimicking the behavior of an expert system
whereas in reinforcement learning the machine learns via direct environment
feedback. Traditional deep reinforcement learning takes a significant time
before the machine starts to converge to an optimal policy. This paper proposes
Augmented Q-Imitation-Learning, a method by which deep reinforcement learning
convergence can be accelerated by applying Q-imitation-learning as the initial
training process in traditional Deep Q-learning.

negative:
The promise of Additive Manufacturing (AM) includes reduced transportation
and warehousing costs, reduction of source material waste, and reduced
environmental impact. AM is extremely useful for making prototypes and has
demonstrated the ability to manufacture complex parts not possible (or
prohibitively expensive) with conventional machining. Scientists and
manufactures are finding increased uses for AM in creation of all types of
finished products including those built from polymers, biological material, and
metals. Although companies such as GE have been using 3D printing for Additive
Manufacturing for over thirty years to make mandrels for light bulb
manufacturing, application areas of Additive Manufacturing have increased
substantially in recent years, particularly due to the reduction in cost of 3D
printers. Like most emergent technologies, there are bound to be growing pains
with AM. This paper looks at the software that supports AM and 3D printing and
their vulnerability to cyber-attacks, intellectual property theft, defect rates
of AM software (which can cause undesired consequences themselves and also
create vulnerabilities that a hacker may exploit), part reliability and safety
of devices incorporating 3D printed parts (when making mission critical parts),
and security/throughput issues of computer networks. Literature searches,
consulting with technical experts and a relatively new hazard analysis
technique will be used, one especially developed for software intensive systems
called Systemic Theoretic Process Analysis (STPA). The purpose of this white
paper is to identify risks (or hazards for mission critical parts) for AM in
this emergent stage so that mitigations can be applied before accidents occur.
A second purpose of this white paper is to evaluate the effectiveness of STPA
as a hazard analysis technique in a field that is still relatively new.

negative:
Additive Manufacturing (AM) is a manufacturing paradigm that builds
three-dimensional objects from a computer-aided design model by successively
adding material layer by layer. AM has become very popular in the past decade
due to its utility for fast prototyping such as 3D printing as well as
manufacturing functional parts with complex geometries using processes such as
laser metal deposition that would be difficult to create using traditional
machining. As the process for creating an intricate part for an expensive metal
such as Titanium is prohibitive with respect to cost, computational models are
used to simulate the behavior of AM processes before the experimental run.
However, as the simulations are computationally costly and time-consuming for
predicting multiscale multi-physics phenomena in AM, physics-informed
data-driven machine-learning systems for predicting the behavior of AM
processes are immensely beneficial. Such models accelerate not only multiscale
simulation tools but also empower real-time control systems using in-situ data.
In this paper, we design and develop essential components of a scientific
framework for developing a data-driven model-based real-time control system.
Finite element methods are employed for solving time-dependent heat equations
and developing the database. The proposed framework uses extremely randomized
trees - an ensemble of bagged decision trees as the regression algorithm
iteratively using temperatures of prior voxels and laser information as inputs
to predict temperatures of subsequent voxels. The models achieve mean absolute
percentage errors below 1% for predicting temperature profiles for AM
processes. The code is made available for the research community at
https://github.com/paularindam/ml-iter-additive.

negative:
We address the problem of solving mixed random linear equations. We have
unlabeled observations coming from multiple linear regressions, and each
observation corresponds to exactly one of the regression models. The goal is to
learn the linear regressors from the observations. Classically, Alternating
Minimization (AM) (which is a variant of Expectation Maximization (EM)) is used
to solve this problem. AM iteratively alternates between the estimation of
labels and solving the regression problems with the estimated labels.
Empirically, it is observed that, for a large variety of non-convex problems
including mixed linear regression, AM converges at a much faster rate compared
to gradient based algorithms. However, the existing theory suggests similar
rate of convergence for AM and gradient based methods, failing to capture this
empirical behavior. In this paper, we close this gap between theory and
practice for the special case of a mixture of $2$ linear regressions. We show
that, provided initialized properly, AM enjoys a \emph{super-linear} rate of
convergence in certain parameter regimes. To the best of our knowledge, this is
the first work that theoretically establishes such rate for AM. Hence, if we
want to recover the unknown regressors upto an error (in $\ell_2$ norm) of
$\epsilon$, AM only takes $\mathcal{O}(\log \log (1/\epsilon))$ iterations.
Furthermore, we compare AM with a gradient based heuristic algorithm
empirically and show that AM dominates in iteration complexity as well as
wall-clock time.