text:
\section{Introduction}

Intelligent systems and automated decision-making are replacing and enhancing human decision-making nowadays to the extent that people are increasingly reliant on them \cite{mohseni2021multidisciplinary}. Despite the increased presence of such systems, people are not often aware that they are interacting with an AI-based system. Recognizing the need for transparency in this evolving policy and technology ecosystem, the ACM U.S. Public Policy Council (USACM) and ACM Europe Council Policy Committee (EUACM) codified a set of principles such as awareness, explainability, accountability, validation and testing to address this \cite{garfinkel2017toward}. Among which, \emph{Explainability}, which could be understood as a description in some form of the functioning of the system, has gained immense traction in the recent past.

Due to their opacity, domains with black-box algorithms like machine learning and deep learning have been extensively researched in the context of explainability. However, the need for explainability goes far beyond  black-box algorithms. For example in various multi-agent systems (MAS), where agents are self-interested, commonly arises a need to aggregate private preferences such as availability, budget constraints and geographical location of several agents into a collective decision in a socially desirable way.  Mechanism design, an important tool in economics and computer science, is one such research topic which is concerned with the development of a mechanism that takes into consideration the preferences of selfish and intelligent agents exhibiting strategic behavior while adhering to norms such as envy-freeness, budget-balancing and pareto-optimality \cite{procaccia2019axioms}.
The applications of mechanism design can be found in various real-world and, in many cases, high-impact applications such as elections, rent division, resource allocation, and stable matching \cite{kominers2019good,procaccia2019axioms}.

Regardless of their extensive usage in the real-world, there is a renewed interest in designing and analyzing mechanisms to align with human values. This includes re-designing existing mechanisms to accommodate human preferences \cite{freedman2020adapting}, viewing existing practices for inclusive housing allocation from a game-theoretic perspective \cite{benabbou2018diversity}, empirical studies on human behavior \cite{tal2015study}, using the insights from empirical analyses to re-frame a mechanism \cite{obraztsova2017doodle}, and devising algorithms to justify the decision of a mechanism \cite{boixel2020automated}. Explaining the results to human participants is a natural and complementary extension to the pursuit of designing mechanisms that are more ``understandable" to humans.

Nevertheless, the road to Explainable Mechanism Design systems is replete with its own share of hurdles.
One key element in providing an explanation in such domains is the goal of the explanation and the measures of its success---whereas with a single user the system's goal is known, hence the explanation aims to improve her recognition of the optimality of the choice made, in settings of mechanism design, a user does not always know the system's goals since they may depend on other agents' preferences.
This focus on preference aggregation of multiple agents, often associated with conflicting goals, may lead to a blatant compromise of the preferences of some of them. The explanations should therefore aim to increase user satisfaction by taking into account the system's decision, the user's and the other agents' preferences, the environment settings, and properties such as fairness, envy and privacy \cite{kraus2020ai}.
In addition to the above intricacies, the presence of domains such as voting, scheduling and resource allocation in popular culture, without the necessity to be theoretically aware, has led to people forming their own irrational opinions which the explanations have to uproot \cite{d2020testing,mccune2019can,uhde2020fairness}.

We also note that even cases where social choice is merely a particular step in a multi-stage decision making process carried out by an agent making decisions on behalf of humans, call for Explainable Mechanism Design. This can hold even when the use of mechanism design is not explicit. Examples of such settings include algorithmic hiring and virtual democracy (an approach to automated decision making) which is used in autonomous vehicles and kidney exchanges to automate moral decisions, and recommendation systems to allocate food donations to recepient organizations \cite{freedman2020adapting,lee2019webuildai,noothigattu2018voting,schumann2020we}.

In this survey, we provide a comprehensive summary of the various threads of explainability in mechanism design. We note that while the broad theme of explainability translates to the same meaning with respect to both machine learning and mechanism design, there are a few differences between the premises of the two fields in terms of what leads up to generating explanations. In particular, we first provide a comparison between Explainable Mechanism Design and Explainable AI (XAI) with respect to the taxonomy, the purpose of explanations and who the explainees are. We then outline the methods of generating explanations in mechanism design. Finally, we elaborate on the challenges of conducting laboratory experiments on explainability in mechanism design and shed light on solution concepts combining insights from XAI and behavioral studies.

\section{Mechanism Design}

Various definitions for mechanism design have been suggested over the years \cite{nisan2001algorithmic,papadimitriou2001algorithms}.
Essentially, a mechanism can be seen as a ``communication system" where the participants send messages to each other and/or to a ``message center" and every collection of messages is assigned an outcome based on a pre-specified rule \cite{hurwicz1960optimality}. These messages are characterized by private information such as utility from an allocation (in rent division), preference over a set of candidates (in social choice theory), or willingness to pay for a good (in auctions). Thus, the mechanism is analogous to a machine that collects, processes and aggregates the private information of several agents in order to reach a desirable social outcome. In most cases the agents are self-interested and rational, and care only about maximizing their private utility with no guarantee that they will tell the truth. Therefore, a desired property of a mechanism is that the agents have no strategic incentive to deviate from truth-telling \cite{lavi2020mechanism}. A mechanism satisfying this property is considered \emph{incentive-compatible}, as every participating agent achieves the best outcome by reporting her true preferences \cite{lavi2020mechanism}.

Since incentive-compatibility is a desired feature, most mechanisms are designed to be incentive-compatible. Hence, even though truth-telling might fetch the least utility, say not winning the Vickrey-Clarke-Groves (VCG) auction \cite{lavi2020mechanism}, it is the best response for an agent in most mechanisms. However, as we observe in section \ref{sec:challenges}, due to repeated interactions, humans may be prone to their own biases. Explanations could be a tool to mitigate user biases as well.

The two main branches of designing mechanisms are the axiomatic branch and the Bayesian branch \cite{carroll2019design}. In the axiomatic branch, the solution is supposed to satisfy a set of desired properties called axioms. Axioms are normative elements designed to conceptualize notions of reason such as fairness, justice and efficiency.
Examples of such axioms include envy-freeness and pareto-optimality. In the Bayesian branch the solution achieves an optimal value for a given objective function such as expected revenue or projected loss.

\section{Motivations for Explainable Mechanism Design} \label{sec:motivation}
Consider the example of a rent division problem with 3 housemates, 3 rooms with a total rent of \$3. Housemate $i$ values room $i$ at \$3 and the other two rooms at \$0. One possible solution to this problem is to assign room 1 to housemate 1 at \$3 and rooms 2 and 3 to housemates 2 and 3 for free. Even though from an inter-personal perspective, this solution seems blatantly unfair to housemate 1, it is an envy-free solution, hence theoretically acceptable. Housemate 1 is indifferent between the three rooms, given their cost, while housemates 2 and 3 are overjoyed \cite{gal2016fairest}. While this is an overblown depiction of the problem with a rent division setting which has seen an immense improvement in the solutions proposed over the years \cite{gal2016fairest}, it is sufficient to illustrate the complexity with respect to devising explanations in mechanism design settings.

As explained in the former section, solutions in mechanism design are obtained by aggregating the preferences of several agents. The nature of the problem necessitates a ``social approach" where the solution is expected to aggregate said preferences in some acceptable manner. This can be achieved by mandating the solution to adhere to certain desirable criteria that are egalitarian in nature, maximizing a criterion of social welfare or using any other method that appreciates the social nature of the problem. The need to balance the preferences of several agents, which could be conflicting in nature might result in the solution not being in favor of a few of them. Multi-user Privacy Conflict due to varying privacy preferences of owners of shared content \cite{mosca2022explainable}, multi-attribute settings such as \emph{team formation} where the solution might not adhere to the preferences of all of the agents with respect to every attribute \cite{georgarabuilding} and settings such as the classical ``glove game" where the solution is non-intuitive yet theoretically sound \cite{nizri2022explainable} are other real-life examples of mechanism design that necessitate a nuanced approach to obtain the solution as well as to devise explanations.

In addition to the social nature of the problem, solutions in mechanism design face two hurdles. First, the issue of what is socially acceptable can vary according to perception, context and domain which can result in multiple solutions for the same problem. For example, in social choice theory, there are multiple voting rules due to the absence of a unique voting rule that satisfies Arrow's mandatory principles of fairness \cite{arrow1951social}. The second problem which is a consequence of the first, is that it is easy for the user to challenge the solution.
This requires the explanations to not only elaborate on how good the solution is but also how problematic another solution is.

This is in sharp contrast with other domains such as machine learning, planning and recommender systems where a solution, good or bad, results from a definite and often complicated algorithm which needs to be broken down for user understanding \cite{chakraborti2020emerging,mohseni2021multidisciplinary,zhang2020explainable}. While solutions in other settings can also rely on metrics such as accuracy, the absence of a unique algorithm in mechanism design settings makes this problem hard as well. Hence, instead of arguing on the theoretical accuracy of the decision or how it results from a particular algorithm which is a common trait in the aforementioned domains, the focus of explanations in mechanism design should be on arguing how the decision is ``good" in its social context and how the preferences of the agents have led to the solution.

\section{Explainable Mechanism Design versus XAI}

While both Explainable Mechanism Design and XAI aim to explain certain decisions made by the system, in retrospect, there are several factors differentiating the two. Owing to the differences in the domains and the solution concepts, there is an obvious difference in the nature of explanations offered in Explainable Mechanism Design and XAI.  One way to reason about the differences in the nature of the explanation to be offered, is by considering the differences in the taxonomy to be used, the explainee and the goals of the explanations. Hence, we focus our comparison in these aspects. Since XAI has become a well established research area, whereas Explainable Mechanism Design is a newly emerging field, we use the first to lead the discussion, mapping and contrasting equivalent notions of Explainability in Mechanism Design accordingly.

\subsection{Taxonomy}

The common types of Explanations that are found in Explainability Studies are \emph{Explanations}, \emph{Justifications} and \emph{Interpretations}. With respect to mechanism design, the most relevant and consequently most researched capability is Justifiability. \emph{Justifications} deal with explaining the system's decision in terms of acceptable societal norms \cite{langley2019explainable}. These norms (i.e. axioms that formalize desirable social concepts such as fairness, justice and efficiency) are the foundation of many mechanisms.  Hence they are natural and, in many cases, effective contenders for explanations \cite{boixel2020automated,nizri2022explainable,procaccia2019axioms}.

While there is no consensus on what constitutes an \emph{Explanation} in mechanism design, we adopt the idea proposed by Langley \cite{langley2019explainable}, who states that \emph{an intelligent system exhibits explainable agency if it can provide, on request, the reasons for its activities}. Still, the extent of research dedicated to this capability is somehow limited (see Table \ref{table:summarytable} later on). As for \emph{Interpretability}, this notion is completely absent from Explainable Mechanism Design.

For example, in the domain of fair division, explaining the decision by highlighting how the decision is envy-free is a \emph{Justification} \cite{lee2019procedural}. However, comparing the maximin solution (the solution that maximizes the minimum utility for every player thus resulting in the least disparity) to an arbitrary envy-free solution to demonstrate the superiority of the former solution amounts to an \emph{Explanation} \cite{gal2016fairest}.

However, in XAI, the relationship of Explanations and Justifications to the algorithm are reversed. Here, \emph{Explanations} act as an accurate proxy of the model while still being understandable to the human users \cite{arrieta2020explainable} and \emph{Justifications} defend the decision of the algorithm by explaining why it is a good one without necessarily focusing on how the decision was made \cite{rosenfeld2019explainability}. While nearly absent from \emph{Explainable Mechanism Design}, Interpretability which aims to enhance user understanding and comprehension of the model's decision-making process and predictions \cite{mohseni2021multidisciplinary} through \emph{Interpretations} \cite{rosenfeld2019explainability} is rigourously researched in XAI. It is also interesting to note that \emph{Justifiability} is the most researched capability in \emph{Explainable Mechanism Design} since there is a need to show how the decision abides by desirable social norms while the need to explain the functioning of black-box nature of the algorithms has led to \emph{Explainability} and \emph{Interpretability} enjoying the most attention from the XAI research community.

\subsection{Explanation Purpose}

Explanations may be provided for various purposes and goals, with partial equivalences between Explainable Mechanism Design and XAI.

\paragraph{Appreciating System Decision-Making.} In Explainable Mechanism Design, an appreciation of the system's decision translates to understanding how the preferences of the different agents are combined to obtain a collective decision \cite{boixel2020automated}. This is akin to understanding how different features contribute to the output in a machine learning model \cite{lai2019human}, which is the focus of XAI. Still, as explained in Section \ref{sec:motivation}, the task of increasing user appreciation of the system's decision making is more challenging in mechanism-design settings, as decisions need to be explained in their wider social context and are often plagued with impossibility theorems \cite{arrow1951social,roth1982economics}.

\paragraph{Improving User Trust and Reliability.} With the decision depending on the preferences of several agents, dissatisfaction is an inevitable evil in mechanism design which might affect the user's trust and reliability. The explanations presented  thus need to argue about the legitimacy of the decision and how, even if the decision is unfair to a particular agent, the mechanism as a whole has adhered to mandatory principles of fairness and the dissatisfied agent needs to make peace with it \cite{kraus2020ai}. In fact, as demonstrated in the work of Suryanarayana et al. \cite{Suryanarayana2022Justifying}, it is often the explanations provided to those participants for whom the winning candidate is the least preferred that are the most impactful.
In XAI, unfavorable situations are also present (e.g., rejection of a loan application), however the prospect of improving the odds of the decision being in her favor through \emph{Counterfactual Explanations} exists \cite{mothilal2020explaining}.  This, unfortunately, cannot be achieved in \emph{Explainable Mechanism Design}.

\paragraph{Ensuring Accuracy of Decision-making.} In mechanism design, explanations can serve as a tool for the verification of results in order to ensure that the decision was made under a set of rules consistently applied in each setting \cite{belahcene2018accountable}. This somehow resembles
the use of XAI as a tool for bias mitigation and fairness assessment \cite{mohseni2021multidisciplinary} in cases where datasets are potentially biased and decision-making might be discriminatory.

\subsection{The Explainee}

The nature and mode of the explanation to be offered depend on who the explainee is and the purpose of providing explanations to her \cite{rosenfeld2019explainability}. One of the main recipients of explanations in \emph{Explainable Mechanism Design} are the end users or participants as they are the ones affected by the decision made.\footnote{In many cases the need to provide users with proper explanations is dictated by the regulator, e.g., in the case of GDPR guidelines \cite{kraus2020ai}.}
Unlike end users in XAI who are typically passive (as the system is making the decision for them, based on their data)
users receiving explanations in mechanism design play an active role in the collective decision process taking place, by reporting their preferences. Examples of such explainees include a researcher whose grant proposal was rejected \cite{belahcene2018accountable}, one among many roommates who is assigned a particular room and rent based on her actively reported (and hopefully true) preferences \cite{gal2016fairest} or, a user in a hybrid domain like algorithmic hiring where a social choice function is applied in some part of the overall algorithm \cite{schumann2020we}.

Two classes of explainees in Explainable Mechanism Design enjoy some similarity with those of XAI. The first one is the \emph{Decision Maker} who does not need to be an expert in mechanism design but has to have relevant knowledge of the domain in order to make informed decisions. This could include the employees of a refugee resettlement agency who need to be able to override the decision proposed by the algorithm \cite{ahani2021dynamic} or the members of a committee who cannot decide on a specific voting rule and base the election on a set of desired properties (axioms) \cite{boixel2020automated}.
The Decision Makers are similar to \emph{Data Experts} in XAI who use explanations to visualize, inspect, tune and select models \cite{mohseni2021multidisciplinary}. The second class is that of an \emph{External Entity}, similar to its namesake in XAI \cite{rosenfeld2019explainability}, who is someone not directly interacting with the system, say an auditor who needs to ensure that the decisions made adhere to a set of rules and that there is no violation \cite{belahcene2018accountable}. In both classes of explainees, the requirements of the explanations to be produced in XAI and Explainable Mechanism Design overlap.

\section{Explanation Concepts}\label{sec:explanationconcepts}

As with XAI \cite{mohseni2021multidisciplinary}, Explainable AI Planning \cite{chakraborti2020emerging} and Explainable Recommendation \cite{zhang2020explainable}, we provide a brief overview of the theoretical aspects (natural contenders) and behavioral aspects (necessary for human comprehension) of the explanations available in literature. Table \ref{table:summarytable} depicts a breakdown of the surveyed papers with respect to the different concepts and the evaluation methods discussed in the next section.

 \begin{table*}[t]
  \caption{Surveyed literature organized by Explanation Concepts -- Normative Characterization (NO), Attributive (AT), Contrastive (CO), Argumentative (AR), Visualization (VI) and Evaluation Methods -- Theoretical Properties (TP), Computational Complexity (CC), Empirical Analysis (EA) and User Studies (US).}

  \begin{tabular}{|p{3.5cm}|p{2.5cm}|p{0.7cm}p{0.7cm}p{0.7cm}p{0.7cm}p{0.7cm}p{0.7cm}p{0.7cm}p{0.7cm}p{0.7cm}|}
\hline
 \multicolumn{11}{|c|}{Explanation Concepts and Evaluation Methods} \\
 \hline

                        Work & Setting & NO & AT & CO & AR & VI & TP & CC & EA & US  \\ \hline
                        Ahani et al. \cite{ahani2021dynamic} & Refugee Resettlement &  &\checkmark &\checkmark & & \checkmark & & & & \\
    \rowcolor[gray]{.9} Belahcene et al. \cite{belahcene2018accountable} & Approval Sorting &\checkmark & & \checkmark & \checkmark& & \checkmark & & &\\
                        Boixel and Endriss \cite{boixel2020automated} & Voting & \checkmark & & & & & \checkmark & & \checkmark & \\
    \rowcolor[gray]{.9} Boixel et al. \cite{boixel2022calculus} & Voting & \checkmark & & & & & \checkmark & & & \\
                        Boixel and de Haan \cite{boixel2021complexity} & Voting & \checkmark & & & & & & \checkmark & & \\
    \rowcolor[gray]{.9} Cailloux and Endriss \cite{cailloux2016arguing} & Voting & \checkmark & & & \checkmark & &\checkmark & & & \\
                        Gal et al. \cite{gal2016fairest} & Rent Division & \checkmark & \checkmark & \checkmark & & \checkmark & & & &  \checkmark \\
    \rowcolor[gray]{.9} Georgara et al. \cite{georgarabuilding} & Team Formation & & \checkmark & \checkmark & & & & & \checkmark & \\
                        Kirsten and Cailloux \cite{kirsten2018towards} & Voting & \checkmark & & \checkmark &  \checkmark & & & & \checkmark &  \\
    \rowcolor[gray]{.9} Knapp \cite{knapp2022justification} & Matching Theory & \checkmark & & & & & \checkmark & & \checkmark & \\
                        Lee et al. \cite{lee2019procedural} & Rent Division & \checkmark & \checkmark & \checkmark & & \checkmark & & & & \checkmark \\
    \rowcolor[gray]{.9} Mosca and Such \cite{mosca2022explainable} & Multi-User Privacy Conflict & \checkmark & \checkmark & \checkmark & \checkmark & & & & & \checkmark \\
                        Nardi et al. \cite{NardiEtAlAAMAS2022} & Voting & \checkmark & & & & &  \checkmark & & \checkmark & \\
    \rowcolor[gray]{.9} Nizri et al. \cite{nizri2022explainable} & Payoff Allocation & \checkmark &  & & & & & & & \checkmark \\
                        Peters et al. \cite{peters2021market} & Voting & & \checkmark & & & & \checkmark & \checkmark & \checkmark & \\
    \rowcolor[gray]{.9} Peters et al. \cite{peters2020explainable} & Voting & \checkmark & & & & & \checkmark & & \checkmark & \\
                        Pozanco et al. \cite{Pozanco2022Explaining} & Scheduling & & \checkmark & \checkmark & & & & & & \checkmark \\
    \rowcolor[gray]{.9} Suryanarayana et al. \cite{Suryanarayana2022Justifying} & Voting & \checkmark & & \checkmark & & & & & & \checkmark \\
                        Zahedi et al. \cite{zahedi2020didn} & Task Allocation& & \checkmark & \checkmark & \checkmark & & \checkmark & & & \checkmark \\

  \hline

 \end{tabular}
 \label{table:summarytable}
 \end{table*}

\subsection{Norms versus Attributes}
As mentioned earlier, mechanism design has two defining characteristics - the private information (such as preference and cost) of the agents participating in it and the requirement for the solution to recognize its social nature. Both of these can be used to devise explanations. Norms that formalize the desirable social traits are the foundation of solutions in mechanism design and can hence be used to extol its virtues. Attributes, on the other hand, quantify the stake a given agent has in the mechanism. Explanations that relate the solution to an agent's individual stakes can be effective in helping her appreciate the impact of the solution from a selfish perspective and thus convince her. For example, convincing a housemate in a rent division setting that the decision is envy-free amounts to a normatively characterized explanation \cite{lee2019procedural} while the comparison of the maximin (the solution that maximizes the minimum utility for every player thus resulting in the least disparity) solution to an arbitrary envy-free solution to demonstrate the lower disparity achieved by the former solution is an attributive explanation \cite{gal2016fairest}. In the following paragraphs, we elaborate on diverse settings where both norms and attributes have been used to devise explanations.

\paragraph{Normative Characterization.}

Formally in mechanism design, axioms are used to capture the social norms that the solution is expected to adhere to.
Procaccia \cite{procaccia2019axioms} advocates for the use of axioms to not only be used for designing a mechanism but also to justify its solutions with an example of the not-for-profit website \emph{Spliddit} \cite{goldman2015spliddit}. Justifying an outcome using a set of agreed upon axioms, without having to depend on a particular rule has found a special appeal in the domain of social choice theory, where no unique outcome can be obtained while following fair voting procedures \cite{arrow1951social}.

In  social choice theory, given a voting profile, Cailloux and Endriss \cite{cailloux2016arguing} developed a logic-based language to construct arguments for and against specific outcomes. Using the elements of the proposed language, an algorithm to justify the Borda outcome given a voting profile was developed. Building on said approach, Boixel and Endriss \cite{boixel2020automated} developed a formal notion of justification based on the definition of Langley \cite{langley2019explainable} and an algorithm based on constraint programming to compute the justifications using any set of axioms. To counter the computational complexity of the aforementioned algorithm \cite{boixel2021complexity}, Nardi et al. \cite{NardiEtAlAAMAS2022} used a combination of instance graphs and state-of-the-art SAT solvers to design an algorithm that can provide viable justifications. To enhance the readability of the justifications using the axiomatic approach, Boixel et al. \cite{boixel2022calculus} developed a tableau-based calculus. Using a combination of SAT solving and Answer Set Programming to implement the calculus, the authors provide an insight into how the justifications look.

The evolution from Boixel and Endriss \cite{boixel2020automated} to Boixel et al. \cite{boixel2022calculus} helps visualize the transformation from a non-automated procedure to an automated procedure, from unstructured justifications to structured justifications and, from manual post processing to obtain the justifications to tableau-based rendering of the justifications for enhanced readability. A demonstration summarizing the application of the aforementioned techniques proposed \cite{boixel2020automated,boixel2022calculus,nardi2021graph,NardiEtAlAAMAS2022} to find justifications given a normative basis can be found in Boixel et al. \cite{BoixelEtAlIJCAI2022}.
Furthermore, the approach used by Boixel and Endriss \cite{boixel2020automated} has been extended to matching theory \cite{knapp2022justification} where an algorithm is designed to justify outcomes that are of interest to a given agent (local outcomes) instead of the whole outcome.

The axiomatic characterization is also used to derive justifications for the results of approval voting \cite{peters2020explainable} and non-compensatory approval sorting \cite{belahcene2018accountable}.   In the broader sense, non-compensatory approval sorting and voting are concerned with aggregating collective information into a single decision. The reviewed literature on justifying the results of a voting mechanism reveal all of the preference ballots. However, Belahcene et al. \cite{belahcene2018accountable} show that the classification based on the binary judgements of the participants is compliant with the decision making process by revealing minimal information that is backed by theoretical properties.

One of the key elements used in some of the papers is Automated Reasoning (AR) using SAT or SMT solvers \cite{belahcene2018accountable,boixel2022calculus,nardi2021graph}. This combination of AR with social choice theory can be used to identify if a voting rule satisfies a particular axiom (thus arguing against it) \cite{kirsten2018towards} as well as verifying the correctness of the system output \cite{boixel2021complexity}.

User studies to test axiom-based explanations were also successful in increasing satisfaction. Suryanarayana et al. \cite{Suryanarayana2022Justifying} tested explanations based on features constructed from axioms in the domain of ranked-choice voting while Nizri et al. \cite{nizri2022explainable} used the axiomatic characterization of Shapley value \cite{shapley1953value} to come up with explanations in the domain of fair division. The research carried out by Nizri et al. \cite{nizri2022explainable} is significant in two ways. First, the solution that is being justified, i.e. Shapley value, satisfies all of the desired properties of fair division \cite{hart1989shapley}. Second, the axiomatic characterization is not only used to justify the solution but also to come up with the algorithm to generate explanations. The authors decompose the coalitional game into sub-games and generate explanations for each of these games based on the additivity axiom which states that the sum of Shapley allocations in each sub-game is equal to the Shapley allocation in the original game. The explanations were successful in convincing the participants that the allocation was fair.

An exception in terms of the norms used can be found in the work of Mosca and Such \cite{mosca2022explainable} in the domain of Multiuser Privacy who propose an explainable agent called ELVIRA that collaborates with other ELVIRA agents to identify the optimal sharing policy for shared content. Here, instead of axioms, the authors use a socio-cultural theory of human values by Schwartz \cite{schwartz2012overview} known as the \emph{theory of basic values}. The explanations however, are based on both values and individual attributes, i.e. the privacy preferences of the participants.

\paragraph{Attributive Explanation.}
If norms capture societal acceptance, attributes quantify personal interests. Hence, explanations that relate to the individual attributes of the participants have also been fruitful in increasing participant satisfaction

Zahedi et al. \cite{zahedi2020didn} compare the \emph{cost} of a proposed allocation to the cost of the counterfactual allocation proposed by the participant. Ahani et al. \cite{ahani2021dynamic} depict the change in \emph{employment score} if the refugee allocation proposed by the algorithm needs to be changed.
A tangential direction termed \emph{priceability} where voters spend money on buying candidates which forms an intuitive explanation for the committee selected in approval-based committee elections was proposed by Peters et al. \cite{peters2021market}. Explaining outcomes based on individual attributes enables the comparison of solutions that are equally good in terms of theoretical requirements. For example, Gal et al. \cite{gal2016fairest} explain their optimal rent division solution by comparing it to another envy-free rent division.

Several explanation generation methods are procedure-agnostic i.e., do not focus on the procedure that leads to the outcome. Here, a framework is developed to encode the different facets of the problems such as explanations, queries and constraints. Notable examples include encoding the Justification Generation Problem for collective decisions into a Constraint Network \cite{boixel2020automated}, developing a generic procedure for providing justifications for Team Formation Algorithm (TFA) while keeping the TFA intact \cite{georgarabuilding} and using Mixed-Integer Linear Programming (MILP) to explain why the preferences of a participants were not satisfied \cite{Pozanco2022Explaining} while designing a preference-driven schedule. While using such frameworks simplifies the process of finding an explanation, adequate care needs to be taken to convert the explanations provided by the system into a readable form. One solution to this problem is using explanation templates \cite{georgarabuilding,Pozanco2022Explaining}.

\subsection{Catering to the Human Mind}

Understanding how human-beings explain and respond to explanations can reveal important insights into how explanations of a system can be presented. Two such behavioral modes of explanations that are considered effective and that have found applications in mechanism design are \emph{Contrastive} explanations and \emph{Argumentative} explanations \cite{miller2019explanation}. Given the fact that mechanism design settings are social in nature, it is imperative that the behavioral nature of the explanations are attended to.
Most of the papers that use a behavioral element in their explanations incorporate an element of social-interaction \cite{miller2019explanation}, which is necessary for a layman to comprehend the functioning of a complicated AI-based system.

As mentioned earlier, mechanism design settings suffer from the issues of familiarity, non-uniqueness of the solution and the solution not being in favor of the participants. This provides the perfect ground for the participants to challenge the solution. Identifying this, there is a great deal of interest in devising \emph{contrastive} explanations that provide reasons for why a particular event did not occur as opposed to why a particular event did  \cite{miller2019explanation}. From Table \ref{table:summarytable}, we can see that in nearly all of the cases where the explanations are based on the individual attributes of the agents, they are contrastive. In these scenarios, contrastive explanations can help the participant compare the difference in her utility across different solutions and thus appreciate the decision better.

In Suryanarayana et al \cite{Suryanarayana2022Justifying}, a contrastive explanation comparing the winning candidate to the participant's most preferred candidate were found to increase user satisfaction and acceptance the most when the winning candidate was the least preferred option of the participant. Similarly, in Mosca and Such \cite{mosca2022explainable}, contrastive explanations were found to be more appealing than general descriptive explanations when the recommended solution was different from the participant's preference.
Contrastive explanations are also especially useful in multi-attribute/multi-preference settings where the outcome may not align with all of the preferences of any participant. Other notable studies that uses the contrastive approach are of Georgara et al. \cite{georgarabuilding} that provide explanations for both collaboration queries (questioning team formation) and assignment queries (challenging the assignment of teams and individuals to tasks) at individual, local and global levels, and the work of Pozanco et al. \cite{Pozanco2022Explaining} which provides contrastive explanations regarding the unsatisfied preferences of the participants while ensuring that the explanation is relevant to the participant.

As far as argumentation is concerned, the presence of umpteen conflicting axioms is an encouraging premise to build an argumentation framework. This is demonstrated by Cailloux and Endriss \cite{cailloux2016arguing} who developed a formal framework for presenting arguments favoring a particular outcome. Zahedi et al. \cite{zahedi2020didn} present the case for the a suggested task allocation by demonstrating how a negotiation based on the counterfactual task allocation proposed by the participant can lead to a higher cost.
Mosca and Such \cite{mosca2022explainable} base their explanation on the argumentation scheme used to obtain the optimal solution. Both in Zahedi et al. \cite{zahedi2020didn} and Mosca and Such \cite{mosca2022explainable} argumentation is used for devising the optimal solution which was then organically extended to generating explanations in favor of the outcome.

\emph{Visualization} is a tool that is often viewed as a less technical means of conveying complex theoretical concepts \cite{barwise1991visual}. Human-in-the-loop systems are a natural extension to mechanism design that caters to capturing reality better. Here, the algorithms have the capacity to process large volumes of data while expert insights are required to handle the inherent uncertainty of the real world. Hence, in addition to enabling easier comparisons \cite{gal2016fairest}, visual tools can also be used to support human decision-making.

Notable illustrations of visualization can be found in the case of the resettlement agency \emph{HIAS} that is involved in resettlement of refugees into communities in the USA. The matching software \emph{Annie}\textsuperscript{TM} \emph{MOORE} enables the employees to override the proposed allocation by revealing the updated statistics so that no change will have a grievous impact \cite{ahani2021dynamic}.

Explaining the outcome through effective visualizations can aid in enhancing the appreciation of fairness, a theoretical notion that is the bread and butter of mechanism design, as was observed by Gal et al. \cite{gal2016fairest}. While Ahani et al. \cite{ahani2021dynamic} capture practical elements such as indivisible families of refugees, batching and, an unknown number of refugee arrivals in the context of refugee resettlement, Gal et al. \cite{gal2016fairest} provide the \emph{fairest} division of rent subject to envy-freeness. In both of these cases, the practical relevance and efficacy of the proposed algorithm is demonstrated with the help of explanations. Hence, user studies with explanations can be seen as a complementary extension to establishing the superiority of novel algorithms while comparing them to existing state-of-the-art algorithms.
It is also interesting to note that
users of the website \emph{Spliddit}, the platform from which data was used by Gal et al. \cite{gal2016fairest}, are provided with a detailed explanation on why the proposed rent division is fair, thus signifying the utility of explanations in everyday usage. Lee et al. \cite{lee2019procedural} used visualizations to both provide an elaborate breakdown of the process as well as let the participant experiment with different values in the website \emph{Spliddit}, to observe the changes. When the participants were shown only their outcome, they perceived the results as unfair while when they were shown the preferences and outcomes for all of the participants in the group, the participants perceived the result as fair.

From Table \ref{table:summarytable}
it can be observed that Normative Characterization and Contrastive Explanations are extensively used in comparison to their other theoretical and behavioral counterparts, respectively.

\section{Evaluation Methods}

There are several dimensions for evaluating methods of Explainable Mechanism Design from theoretical as well as practical perspectives. We provide a description on each of them in the following paragraphs.

\paragraph{Theoretical Properties.} Building explanations on the foundation of concepts like Axiomatic Characterization, Logic-based Programming and Automated Reasoning necessitates these methods to be supported by rigid theoretical norms. Examples include proof of an explanation given the problem instance \cite{cailloux2016arguing,zahedi2020didn}, uniqueness of the outcome and justification given a voting profile and normative basis \cite{boixel2020automated}, the correctness of a tableau-based calculus for generating explanations \cite{boixel2022calculus} and an upper bound on the number of steps required to generate justifications to ensure readability \cite{peters2020explainable}.

\paragraph{Computational Complexity.} Practical feasibility of any explanation-generation method is tantamount to its real-life application. Very few authors have addressed this aspect in their work. Exceptional examples include, Peters et al. \cite{peters2021market} that demonstrate the polynomial-time verifiability of their proposed heuristic algorithm. Boixel and de Haan \cite{boixel2021complexity} prove the intractability of finding and generating justifications given a normative basis.

\paragraph{Empirical Analysis.} Empirical insights act as an intermediate between theoretical guarantees and experimental results. Running the explainability studies on real or synthetic datasets can help compare the running times of several explanation generation methods and pick the fastest one \cite{nardi2021graph}, help understand the step-by-step breakdown of the explanation generation method \cite{peters2020explainable}, disclose interesting insights about different statistical cultures (e.g., probability distribution of election profiles) that might help in the development of personalized explanations \cite{nardi2021graph}, and identify and evaluate metrics for the evaluation of explanation-generation methods before deploying them in real-time studies \cite{georgarabuilding}.

\paragraph{User Studies.}
User studies are an effective means for examining the consequences of explanations on aspects such as reliability, satisfaction, trust and conviction.
While it is always desirable to conduct the experiment with the actual participants of a mechanism as in the case of \emph{Spliddit} \cite{gal2016fairest}, experiments conducted with synthetic data using platforms such as Amazon Mechanical Turk (AMT) or laboratory settings are a great starting point \cite{nizri2022explainable,Suryanarayana2022Justifying}.

In addition to the obvious purpose of helping determine the impact of explanations, user studies can also be used to provide insights on curating effective explanations. For example, Suryanarayana et al. \cite{Suryanarayana2022Justifying} hinted at a user bias in favor of plurality voting rule while Mosca and Such \cite{mosca2022explainable} used the experimental insights to devise a hybrid explanation framework and improve the wording of the explanations.

From Table \ref{table:summarytable} we can observe that there is a good mix of all of the evaluation methods in the literature. However, performing \emph{Empirical Evaluation} and \emph{User Studies} to evaluate explanation-generation methods in mechanism design is challenging and we address this issue in detail in the next section.

\section{Challenges and Possible Solutions}\label{sec:challenges}
Despite the rapid increase in interest in explainable systems for mechanism design, the progress made in this field is still far behind compared to XAI. One of the main challenges is that of testing. As far as testing for the efficacy of explanations is considered, the ideal premise would be testing with real users, as in \emph{Spliddit} \cite{gal2016fairest} (where  explainees are the actual renters in a rent division setting) or in the work of Pozanco et al. \cite{Pozanco2022Explaining} (where a real restrictive \emph{return to office} scenario due to the COVID-19 pandemic was tackled).
However, the development of such evaluations is expensive and users of such real-life settings are often inaccessible to the research community. We therefore outline a few challenges in designing nearly realistic experiments that can be conducted in laboratories or platforms like AMT.

\paragraph{User Behavior.} Human participants in a mechanism are poles apart from the perfect agents modeled in theory and exhibit short-sighted and downright irrational behavior. Instances of such behavior include reward divisions that adhere to weaker axioms than those that characterize Shapley value \cite{d2020testing}, playing dominated strategies in cooperative settings such as fair division and bargaining \cite{10.1145/3328526.3329592}, and performing manipulations that can be captured by simple heuristics \cite{mennle2015power}.
Any \emph{social} explanation \cite{miller2019explanation} thus catering to the expected selfish interests, while the participants betray the same, may defeat the purpose of explanations. For example, in an experiment conducted on human behavior in voting, Tal et al. \cite{tal2015study} report that the participants exhibit herding by disregarding their most preferred candidate and voting for the candidate with the most first place votes in a predictive poll, even though it is the least optimal choice for them. In this case, framing a contrastive explanation comparing the participant's most preferred candidate and the winning candidate (which might be the candidate the participant voted for) would be counter-productive.

 A natural solution to the problem of mismatched behavior is building predictive models using behavioral, game-theoretic and machine learning tools. Examples include models for predicting human decisions in plurality voting \cite{fairstein2019modeling}, approval voting \cite{scheuerman2021modeling} and auctions \cite{noti2021bid}. The benefits of such models are twofold. First, it might help the explanations to be more \emph{selective} \cite{miller2019explanation} by shedding light on what is important to the explainee. For example, in the game-theoretic model of human behavior in Doodle Polls \cite{obraztsova2017doodle}, the concept of \emph{Social Bonus} is proposed in order to reason why voters vote for unpopular slots.
Consequently, contrastive explanations comparing the winning candidates to the unpopular ones can be discarded as the latter are insignificant to the voter.

The second and rather consequential utility from such models is that they might help identify the sub-optimal manipulations of the participants which can be contrasted with the optimal choice. In that context, an interesting hypothesis to investigate is if and what kind of explanations can bring irrational humans closer to the rational agents modeled in literature. This will open new avenues for \emph{Interpretability} in mechanism design which has not received as much attention as in the XAI literature \cite{rosenfeld2019explainability}.

\paragraph{User Biases.}  The prevelance of mechanisms in society has led to humans forming their own prejudices such as favoritism for plurality voting rule \cite{mccune2019can,peters2020explainable}, altruism towards non-performing participants \cite{d2020testing} and a disdain towards algorithmic decisions as being far from reality \cite{lee2017algorithmic,mckelvey1992experimental,uhde2020fairness}.

Long before building explainable systems was considered, researchers invested efforts into manually explaining technical jargon to non-expert participants. Notable examples include acquainting participants of a centipede game with backward induction \cite{mckelvey1992experimental} and measuring the frequency of violation of fairness criteria in voting \cite{mccune2019can}. Coupling these ideas with biases such as \emph{automation bias}, where a user believes that a computing system is more knowledgeable and ``intelligent" than it is, is a direction worth exploring \cite{goddard2012automation}. In addition, comparing different modes of presenting explanations such as textual and visual, both of which are extensively used in Explainable Recommender Systems \cite{zhang2020explainable}, can strengthen the efficacy of explanations.  It is also noteworthy that human intelligence can be leveraged to not only rate explanations but also to provide explanations, thus providing valuable insights into human factors that might be useful for generating convincing explanations \cite{Pozanco2022Explaining,Suryanarayana2022Justifying} .

\paragraph{Lack of Data.} A useful tool in bridging the gap between idealized agent behavior and flawed human behavior is empirical analysis and subsequent modeling of human participants in the different mechanism design settings. Also, as mentioned earlier, empirical analysis can act as an intermediate stage between theoretical guarantees and user studies while revealing interesting insights.

However, there are not many datasets in the domains of Computational Social Choice and Preference Reasoning publicly available \cite{mattei2021closing}. Also, while collecting, preserving and presenting data on private preferences, adequate care needs to be taken to ensure that user privacy is preserved \cite{kraus2020ai}.

Naturally, the obvious solution to the lack of data is to develop tools for efficient data collection. One such very useful collection of datasets in the domain of Computational Social Choice is \emph{Preflib}\footnote{\url{https://www.preflib.org/}} which was used by Nardi \cite{nardi2021graph} to examine the practical utility of the proposed algorithm. However the process of data collection is easier said than done. Replicating real-life settings in order to get people to report their preferences, even manipulated ones, is a mammoth task. Anonymizing data is an effective way to protect the privacy of the participants. An alternate technique to preserve privacy was used by Gal et al. \cite{gal2016fairest} where the original valuations for the rooms were perturbed by an acceptable margin and presented to the explainees.

\paragraph{Simulating Synthetic Environments.} In order to evaluate the efficacy of explanations, it is vital to have the participant interested in the explanations. In mechanism design, these interests are captured by notions such as preferences, utility and costs which are easy to conceptualize but difficult to replicate and/or induce in lab experiments. XAI, even though tasked with explaining complex algorithms, enjoys relatability with experiments such as image classification \cite{nourani2019effects}, review classification \cite{lai2019human} and selection of a competent agent \cite{amir2019summarizing}. This enables the design of interactive experiments where explanations can be sneaked in without being explicit, hence eliciting an organic response from the participant.

Inspired by the experimental design in XAI, gamification of the problems is a good starting point. Tailoring games such as the centipede game for bargaining \cite{mckelvey1992experimental} and share-the-loot game for resource allocation \cite{d2020testing} to accommodate explanations and with the reward tied to the performance of the participant is an idea worth testing. The presence of a monetary reward inadvertently engrosses the participant, thus eliciting a realistic response. Some other tested methods of invoking user interest in synthetic lab experiments were done using the concept of bonus from the winning candidate in ranked choice voting \cite{Suryanarayana2022Justifying} and asking the participant to imagine themselves in the setting and extracting their preferences through meticulously designed questionnaires \cite{mosca2022explainable}.

Another way to stimulate the interest of participants in explanations might be to leverage the diversity of axioms to build argumentation systems augmented with human input on how convincing the arguments are \cite{rosenfeld2016providing}. The conversion from passive listeners of explanations to active debaters of arguments might trigger a passionate yet honest response from the participants.

In addition to the above ideas, tools used in Social Psychology such as \emph{Experimental Vignette Methodology (EVM)} \cite{aguinis2014best} and online testing methods like A/B testing used in Explainable Recommendation \cite{zhang2020explainable} offer valid premises for developing tests for Explainable Mechanism Design.

\section{Conclusion}

In this paper, we survey explainability in mechanism design, provide an overall picture of the various concepts around it and shed light on the challenges faced by researchers in the domain.

While we do propose several workarounds to overcome the aforementioned challenges, we emphasize that implementing each of these is a non-trivial task per se and calls for collaborations between researchers in mechanism design, human-agent interaction, software engineering, and psychology. We hope that both experienced as well as budding researchers find this survey helpful in designing and improving explainability in mechanism design. We also envision a future where designing mechanisms aligned with human values and Explainable Mechanism Design complement each other.

\section*{Acknowledgements} This work was supported in part by the Data Science Institute at Bar-Ilan University, the EU Project TAILOR under grant 952215 and the Israeli Ministry of Science \& Technology under grant 89583. The research was carried out with the technological support and funding from the HRI Consortium -- the Israel Innovation Authority. Sharadhi Alape Suryanarayana is grateful for the President's Scholarship and Erasmus+ Global Mobility Programme that has supported this research.

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Marcovitch & Associates
About Marcovitch & Associates
Dr. Sharon Marcovitch has a Master of Arts Degree in School Psychology from the Ontario Institute for Studies in Education, a Master of Arts Degree in Education and Psychology from the University of Michigan, and a Doctorate in Developmental Psychology from York University. She was Co-Director of the Developmental Evaluation Unit at the Hospital for Sick Children for 15 years. She was an Assistant Professor in the departments of Pediatrics and Medicine, and Associate Faculty in the departments of Applied Psychology and Educational Theory at the University of Toronto. In these capacities, she has taught courses in Child Development, Social Development, Exceptionalities of Childhood and Psychological Assessment. Dr. Marcovitch provides training to staff within Boards of Education and Consultation to IBI programs for children with Autism. Dr. Marcovitch has been offering services through private practice since 1995.
Dr. Howard Marcovitch has a Doctorate in Applied Psychology from the Ontario Institute for Studies in Education. He was Chief Psychologist of The Scarborough Board of Education for 19 years and was Central Coordinator for Psychology, Social Work, Speech Language Pathology, Occupational and Physiotherapy, and Psychiatry services for the Toronto District School Board. He has also taught courses at the University of Toronto in Psychological Assessment and Childhood Psychopathology. He is a recognized expert in the field of education and special education, with direct experience in the interpretation and application of Ministry of Education policies and procedures, program development, assessment design and student placement.
Pamela Wylie has a Master of Science in Psychology from McMaster University, and completed her undergraduate in degree in Neuroscience and Psychology at the University of Toronto. She completed her clinical training at Chedoke-McMaster Hospital. She has extensive experience in the assessment and treatment of children, adolescents and adults with various developmental and clinical issues, including roles at Toronto Western Hospital, private practice and Columbia Neuro-Rehabilitation Services, where she also served as Clinic Director. At Marcovitch & Associates, Pamela specializes in diagnostic assessments of children and adolescents with attentional, developmental and learning issues, as well as career exploration and planning.
Sanaz Mehranvar received her Ph.D. training in Clinical-Developmental Psychology from York University and is currently working on completing her doctoral dissertation. Sanaz holds a Master of Arts degree from York University and has undertaken clinical training in a variety of multidisciplinary settings including the Centre for Addiction and Mental Health, Bloorview Kids Rehab, and the Department of Clinical Health Psychology at the Faculty of Medicine at the University of Manitoba. Sanaz has extensive training and experience conducting diagnostic, psychoeducational, and social-emotional assessments for young children, adolescents, and young adults with a variety of diagnoses. She also uses a variety of evidence-based treatment approaches (e.g., cognitive-behavioural therapy (CBT), solution-focused brief therapy (SFBT), psychodynamic, and family systems) to treat children, adolescent and families who are dealing with mood, anxiety, and behavioural problems. At Marcovitch & Associates, Sanaz specializes in diagnostic assessment of children with attentional, developmental, and learning issues as well as providing therapy to children and their parents with a wide range of presenting problems.
Sabrina McHugh-Thomas earned a Master of Arts degree in Counselling Psychology from McGill University and received her Bachelor of Arts (Honours) degree in Psychology from Carleton University. Sabrina has extensive training and experience working with children, adolescents and young adults by providing psychotherapy in addition to comprehensive psychoeducational and socio-emotional assessments. She has worked with a diverse clientele in various settings including elementary and secondary schools, universities, long-term residential facilities and in private practice. Sabrina has provided treatment of mood and anxiety disorders as well behavioural issues that affect youth and their families and has conducted assessments for ADHD, learning disabilities and a variety of behavioural and interpersonal issues. At Marcovitch & Associates, Sabrina specializes in diagnostic assessments of children and adolescents with socio-emotional, attentional, behavioural and learning issues, as well as providing therapy using a variety of evidence based approaches (e.g., cognitive-behavioural therapy (CBT) and dialectical behaviour therapy (DBT)) to treat a variety of presenting concerns.
Carla Freedman, who has her diploma in Human Resources Management, has been providing Executive and Administrative support to organizations for over 25 years. In her role as Office Manager, Carla has been with Marcovitch and Associates since August 2005.
Marcovitch & Associates, a division of Marcovitch Psychology Professional Corporation.
491 Lawrence Avenue West, Suite 405, Toronto ON, M5M 1C7

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"Football is played in pads, so to put the pads on and watch the guys bang a little bit is always fun," coach Vance Joseph said. "For the running game, and obviously stopping the run, the pads are critical."
Left tackle Garett Bolles and inside linebacker Brandon Marshall were among the casualties Monday.
"Garett Bolles took a shot to the chin. He's being evaluated for a head injury," Joseph said, adding that Marshall "had a minor wrist injury. He's OK."
Cornerback Marcos Rios sat out with a sore hip and tight end Jeff Heuerman sat out with a sore knee, which Joseph said was strictly precautionary.
The Broncos are also dealing with second-year wide receiver Carlos Henderson, who failed to report to training camp while dealing with what the team calls a family situation.
Although Joseph has said it's up to Henderson whether his career will continue, several members of the organization are keeping in touch with their absent teammate.
"Lots of guys have called Carlos. (Wide Receivers) Coach Zach (Azzanni) has been calling him, I've been calling Carlos, his teammates have been," Joseph said. "Obviously, he's having some family issues he's working through and it's really his timeline.
"He can return any time he gets ready to return. It's on him, but his guys are supporting him, the coaches are supporting him. So we're looking forward to having him back."
Henderson was a third-round draft pick out of Louisiana Tech last year but he missed his rookie season with a hand injury. He missed much of the offseason program this year with a pulled hamstring and is a long-shot to even make the 53-man roster if he does return.
Joseph said he has no timeline for Henderson to get back.
"We've had some conversations about other things than football. He's not ready for football right now, so we're not talking about football a lot," Joseph said. "We're doing a good job as a team of checking on him on a daily basis."
Things should ratchet up overall Tuesday when the Broncos also wear their thigh pads.
"Any time we're full pads, it's like game day," Joseph said. "Every pad's on, mouth guard, chin straps buckled, so it'll look different as far as what they're wearing, but the practice shouldn't look that much different as far as the physical part of it."
Nose tackle Domata Peko, for one, appreciated finally getting to do some hitting in the trenches Monday.
"That what it's about, you know? We don't play without shoulder pads, so when you get them on that's when you really get to get real physical and do your job," Peko said. "Especially up front. In the trenches, you've got to have the pads on and feel that contact to see where you're at and sharpen each other up.
"It was a good first day of shells today. They call it shells, and then full pads tomorrow, but in the trenches, every time we put the pads on it's pretty much full pads. It felt good today."
The Broncos will also be in full pads Wednesday.


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By Randi Nord @randinord
WASHINGTON (Analysis) — Washington's hostility towards the Islamic Republic of Iran dates back nearly 40 years to February of 1979, when revolutionary forces overthrew the Western-backed monarchy of Mohammad Reza Shah.
The United States consistently maintains that its involvement began with the hostage crisis in 1979 and continues today due to Iran's ballistic missile and nuclear program, as well as meddling throughout the region in places like Syria, Lebanon, and now Yemen (albeit without evidence in some cases).
What the media and Western governments don't mention is that Iran's core ideology stands directly opposed to U.S. military and economic expansion. The Islamic Republic's promotion of self-determination indeed poses an existential threat to Washington's dominance throughout the entire region — similar to that of communism during the Cold War.
The vilification of Iran through the military-industrial-media complex runs deep. So deep that they've successfully portrayed Iran as a sort of Shia version of Saudi Arabia.
However, the Islamic Republic of Iran is nothing like the Kingdom of Saudi Arabia. Despite the media's almost childlike ignorance, Tehran and Riyadh stand at direct odds due to pervasive ideological differences rather than simple Sunni-Shia sectarian disagreements.
But why is Tehran such a thorn in Washington's side and why have tensions recently increased?
To answer this, it's important to understand the key ideological differences between the United States and Iran, as well as how these differences play out on the geopolitical landscape.
A clash of ideologies: imperialism vs. self-determination
An Iranian woman walks past a mural depicting Iranian armed forces in the battlefield, at Palestine Sq. in Tehran, Iran, Jan. 16, 2016. (AP/Vahid Salemi)
Even independent news outlets often fail to grasp the reasons behind Washington's constant targeting of Iran — pointing simplistically to oil and gas. While resource theft has been a significant factor behind Washington's foreign policy, it alone is not sufficient motivation to promote "regime change" for 40 years.
The true conflict stems from Tehran and Washington's differences in ideology (and no, it's not Christianity versus Islam). It's a conflict between imperialism and self-determination.
The U.S. status as world superpower relies on its ability to exploit and manipulate competition while propping up what essentially amounts to an empire through military quests. The United States uses military, political and economic imperialism to control populations from the Middle East to Latin America.
Even the population within the empire is not immune, U.S. citizens face police brutality, labor exploitation, and tax extortion to fund empire abroad. Several oppressed groups exist inside the United States (such as African-Americans and indigenous peoples), which provide a micro-scale example of how Washington deals with foreign entities it views as inferior.
When the US Pledged Not to Intervene in Iran's Internal Affairs
The MEK: From Revolutionary Group to Imperialist Asset
The Media's Wildly Different Take on Unrest in Tunisia and Iran
While the United States often functions as an oppressor, an opposing ideology is the backbone of Iran's constitution: self-determination.
As Point 6c in Article 2 of Iran's Constitution states:
The Islamic Republic is a system based on the faith in the wondrous and exalted status of human beings and their freedom, which must be endowed with responsibility, before God. These are achieved through: the negation of all kinds of oppression, authoritarianism, or the acceptance of domination, which secures justice, political and economic, social, and cultural independence and national unity."
To achieve this goal, Article 3 states that Tehran will devote resources to "unrestrained support for the impoverished people of the world" and "the complete rejection of colonialism and the prevention of foreign influence."
Iran's foreign policy focuses on unrelenting support for the oppressed, and refusal to accept domination culturally, economically, and militarily. That's precisely why Iran unconditionally supports Palestine against Zionism, as well as other nations under the thumb of U.S. domination.
Ph.D. candidate, university lecturer, and political commentator Marwa Osman, based in Beirut, Lebanon, asserts U.S. foreign policy goals regarding Iran have little to do with national security:
The U.S.'s attempts to put further sanctions on Iran or possibly even start a war with Iran have nothing to do with safety or US national security as consecutive administrations have emphasized since 1979 and everything to do with protecting corporate interests.
Iran has the third largest oil reserves and second largest natural gas reserves in the world. U.S. foreign policy has been centered on control of the world's energy reserves, while the four major recipients of Iran's oil are all from Asia, which is very much unacceptable to Western policymakers with national interests in mind.
The economic sanctions proposed by the U.S. would cripple the Iranian economy and surely it would not be long before political and domestic turmoil to grow out of hand. This would offer the U.S. and its allies the chance to enter the country with the goal of 'spreading democracy."
Syria as a breaking point and the curious case of Yemen
Thousands of Iranians attend the funeral of Mohsen Hojaji, an Iranian soldier beheaded by ISIS in Syria, in Tehran, Iran, Sept. 27, 2017.(AP/Ebrahim Noroozi)
Syria has manifested as a breaking point for relations between Tehran and Washington.
The United States launched its proxy war against Syria for a variety of reasons, one of which included replacing Syrian President Bashar al-Assad with an Israeli-friendly regime. As part of warming relations with Israel, Washington's ideal Syrian government would cease relations with Iran and cut off cooperation with Hezbollah.
An email published by WikiLeaks reveals an exchange between Hillary Clinton and her aides which includes the subject line "an interesting proposal from Bruce Riedel re: how Israel could help get Assad out of office.":
Ephraim Halevy, the former head of Israel's secret intelligence service, Mossad, has rightly argued that toppling Assad and weakening Hizbullah is a far more important and strategic opportunity for Israel today than a military strike on Iran's nuclear facilities."
Isolating Iran was always one of Washington's primary objective in its war against Syria.
The email describes hypothetical negotiations that include Syria gaining full control of the Golan Heights on the condition Assad step down in favor of a government that recognizes Israel while ceasing support for Iran and Hezbollah.
That plan didn't work out as hoped.
In fact, it drastically backfired: Syria has strengthened its relationship with Iran and Hezbollah, and those entities are now battle-tested.
Iran's Islamic Revolutionary Guard Corps (IRGC), Hezbollah, and Iranian-backed militias played a crucial role in supporting the Syrian Arab Army against U.S.-backed proxies. Indeed, if it weren't for Iran's support, the Syrian landscape would look vastly different today.
Not only has Iran supported the Syrian Arab Army against U.S.-backed proxies, but its militias have dislodged and nearly eliminated ISIS and other terrorist groups throughout Syria and Iraq. Osman had this to say about Washington's reaction to Iranian policy in the region:
Nowhere is Iran projecting its regional power more broadly than in Syria. … This only made Trump push for a further aggressive approach to try to contain Iran. I think what worries the Trump administration is that, with these gains, Iran and its allies will carve out what the U.S. calls a 'Shia crescent' extending from Iran, through Iraq and Syria, and into Lebanon, where Hezbollah is the most powerful political and military force.
Such a viewpoint appears threatening not only for the Trump Administration, but also its allies in the Arab world, especially the KSA and the Israeli entity. According to the recent developments this past week, combined with Tillerson's statement, it's obvious that the next line of attack is going to be the northern border of Syria with Turkey."
Syria and Lebanon are obvious hotspots, but Washington's vilification of Iran through its purported support of rebel fighters in Yemen raise far more pressing questions.
No tangible evidence exists to prove Iran supplies Ansarullah (the Houthis) with weapons, as U.S. Ambassador to the United Nations, Nikki Haley recently asserted. Nonetheless, the United States recently labeled Ansarullah an "Iranian-backed militia" in nearly every media report (or simply a "Shiite militia" to imply Iranian influence).
The New York Times went so as far as to call Ansarullah an extension of Hezbollah:
The network Hezbollah helped build has changed conflicts across the region. In Syria, the militias have played a major role in propping up President Bashar al-Assad, an important Iranian ally. In Iraq, they are battling the Islamic State and promoting Iranian interests. In Yemen, they have taken over the capital city and dragged Saudi Arabia, an Iranian foe, into a costly quagmire. In Lebanon, they broadcast pro-Iranian news and build forces to fight Israel."
The Times does not, however, explain Tehran'sability to smuggle weapons into Yemen during a U.S.-enforced land, sea, and air blockade.
The United States knows it is operating in a bipolar world: a nation or group in the Middle East that doesn't ally itself with the United States and Saudi Arabia will likely build relations with the opposing axis, which effectively means Iran, Syria, and now Qatar. Although Ansarullah began as a Zaydi-Shia movement, it has since morphed into a broad coalition consisting of Sunnis, Shias, as well as various local tribes and political parties that oppose U.S. imperialism, Zionism, and economic exploitation.
This prospect troubles the United States and Saudi Arabia. If a small Yemeni movement can resist and become self-determined, what's to stop citizens in Saudi Arabia, Bahrain, and elsewhere from getting such ideas? The mere possibility that Ansarullah could ally with Iran is enough for the United States to allege the relationship already exists, and to carry out a devastating military response.
Over 35,000 civilians have been killed or wounded by Riyadh's U.S.-backed military aggression and siege against Yemen, based on nothing more than the idea that they could possibly make their own choices.
Iran is not a "Shia" Saudi Arabia
A member of the media looks at remains of cars and buildings in the Shia-majority Saudi town of Awamiyah on August 9, 2017, following a crackdown by the Saudi government forces. (Photo: Reuters)
The barrage of negative press surrounding Iran serves two purposes: defaming Tehran and normalizing Riyadh.
Yet the Islamic Republic of Iran and the Kingdom of Saudi Arabia are nothing alike — even before considering the obvious religious differences.
Iran is a theocratic republic with a constitution containing democratic elements. Citizens, both male and female, elect leaders and representatives into office through a well-defined electorial process. Despite media portrayal to the contrary, Iranians are guaranteed human rights through their constitution, including the freedom to practice any religion, freedom of assembly, and a legal presumption of innocence.
Iranians also enjoy a robust social welfare system which either provides or subsidizes housing, higher education, food, healthcare, unemployment insurance, and physical training. Many of these benefits are constitutionally guaranteed: a constitution designed on the Islamic principle of a fair and just economic system.
The same, unfortunately, cannot be said for citizens of the Kingdom of Saudi Arabia.
It's true that a subset of Saudi citizens enjoy social welfare benefits on the back of a lucrative oil economy, and do not pay taxes, benefits which have kept the Saudi population relatively docile. This, however, will likely change soon as Riyadh moves towards pervasive privatization.
Saudi Arabia does not have a constitution nor does it guarantee even basic human rights. Instead of a constitution, the Kingdom employs "basic law," a concept that's typically utilized on a temporary basis. In the Kingdom, basic law is determined by the country's hardline Sunni interpretation of the Qur'an and the Salafist interpretation of Sharia (Islamic law) and Sunnah (traditions).
Citizens are not free to practice any religion — even Shias, and Sunni Muslims who do not adhere to Wahhabism or Salafism, face persecution. In Saudi Arabia there are no synagogues as all religions other than Islam are banned. Iran, on the other hand, has about 60 synagogues for its Jewish community, the largest in the Middle East outside of Israel.
Iranian Jews pray at the Molla Agha Baba Synagogue, in the city of Yazd 420 miles (676 kilometers) south of capital Tehran, Iran. (AP Photo)
Perhaps the most striking contrast between the two countries is the status of women. Women comprise 70 percent of Iran's science, tech, and engineering students. In fact, Iran enrolls more women in manufacturing, engineering, and construction than any country in the world — nearly double that of the United States, despite having a much smaller population (323 million total population in the U.S. and 80 million in Iran). As of 2012, 476,039 Iranian women were enrolled in higher education in these fields compared to 262,840 women in the United States.
Women in Saudi Arabia may not leave home without a male guardian's explicit permission at the risk of draconian punishments such as beheading or stoning. Adultery is a death sentence for Saudi women, who won't have the right to drive until recent reforms are enacted later this summer. The rights of women are mentioned 12 times in Iran's constitution, while Saudi Arabia's basic law fails to mention word "women" even once.
As Osman told MintPress News, the Iran depicted through popular media tropes is a far cry from reality:
The reason behind this behavior is that the Western mainstream media knows that the easiest way to gain support with their audiences back home and abroad for all the hostilities against independent nations that are opposing them, is to smear and demonize them.
In the eyes of many people, Iran is a nation of Islamic fundamentalists with a sole purpose of destroying 'Western' and 'civilized' values. … News stories about the country are often accompanied by photos of burka-clad women walking past a graffiti illustrating the Statue of Liberty with the face of a skull on the wall facing the former U.S. Embassy.
But this is not the real Iran. I have been to Iran and have one thing to say about it: Pack your bags and go see it, you can thank me later for the great experience you will have, the amazing and generous people you will meet and the astonishing history you will learn about the country."
A thorn in Washington's side
Huge crowds attend a pro-government rally in the southwestern city of Ahvaz, Iran, Jan. 3, 2018. (Mehdi Pedramkhoo/Mehr/AP)
The United States has levied sanctions and other restrictions against Iran in some way or another for nearly 40 years.
Despite this, the Republic has not only survived but thrived. Infant mortality rates are at all-time lows. UNICEF calls Iran's post-revolution healthcare system "excellent" for meeting the needs of both urban and rural citizens of all income levels.
As with any country crippled by decades of sanctions, Iran's economic situation is far from ideal. However, it is improving.
The failure of the U.S. sanctions to curb Iran's growth has the United States under President Donald Trump scrambling to reduce Iran's influence and domestic gains by going after the Joint Comprehensive Plan of Action (JCPOA, known commonly as the Iran nuclear deal).
What makes Trump's decision to backtrack on the nuclear agreement so difficult to carry out?
After sanctions relief was first enacted in 2015, U.S. allies in Europe jumped at the chance to invest in – and conduct business with Iranian entities, meaning Iran is now not only an enemy of Washington militarily and ideologically, but is now an economic competitor.
Iran's unapologetic self-determination, including its ballistic missile and nuclear energy program, resistance to economic imperialism, and exportation of this powerful ideology by its support of oppressed nations, makes the Islamic Republic a constant thorn in Washington's side.
Top Photo | Thousands of Iranians attend the funeral of Mohsen Hojaji, an Iranian soldier beheaded by ISIS in Syria, in Tehran, Iran, Sept. 27, 2017.(AP/Ebrahim Noroozi)
Marwa Osman is a Ph.D. candidate located in Beirut, Lebanon. She is a University Lecturer at the Lebanese International University and Maaref University and former host of the political show "The Middle East Stream," broadcast on Al-Etejah English Channel. She is also a member of the Blue Peace Media Network and political commentator on issues of the Middle East on several international and regional media outlets, including RT, Press TV, Al Manar and Al Alam. And she is a writer in several news websites including RT, Khamenei.ir, Fort Russ, Shafaqna, Italian Insider, AHTribune.
Randi Nord is a journalist and co-founder of Geopolitics Alert. She covers U.S. foreign policy in the Middle East with a special focus on Yemen.
Imperialisim


The previous facility closed after a national accrediting agency found examples of slipshod work and botched evidence testing there, but the problems dated as far back as 2003.
Nassau County plans to open a new Westbury crime lab in late June, following more than a decade of critical errors that led to the closing of the old police forensic facility.
The previous facility was closed in 2011 after a national accrediting agency found examples of slipshod work and botched evidence testing there, but the problems dated as far back as 2003.
"It's been a difficult history," County Executive Laura Curran said Wednesday during a tour of the Westbury facility with District Attorney Madeline Singas, Police Commissioner Patrick Ryder, Legis. Rose Marie Walker and Pasquale Buffolino, the lab's director.
The $24.4 million crime lab will be about four times the space of the previous East Meadow lab, and officials are hoping to get accreditation for other disciplines, such as crime scene reconstruction, trace evidence and ballistics testing within about a year, according to Buffolino.
"It's a long time coming for us," Buffolino said.
Officials called the new location "state-of-the-art" and said it includes a functioning firearms range, a 24-hour evidence intake area and a crime scene reconstruction garage. Construction began in 2015. The lab's annual operating budget is $3.4 million, plus $807,000 in grant funding.
"We are prepared to meet the future," Singas said.
The county currently tests certain evidence at the medical examiner's office in East Meadow, but others are outsourced to the State Police, as well as facilities in Westchester and Pennsylvania. The outsourcing is expensive, officials said, because the experts must often be brought to Nassau County to testify in court.
The American Society of Crime Laboratory Directors' Laboratory Accreditation Board, a North Carolina organization that accredits crime labs for state agencies, put the Nassau facility on probation in August 2006, following a 2005 review. Problems found in 2005 and 2010 included lab employees — both civilians and police detectives — who did not follow protocols that possibly compromised test results, equipment that was not regularly maintained to ensure accuracy, and analysts who failed proficiency exams or were not tested at all.
Nassau officials closed the lab in 2011, and dozens of court filings followed from defendants seeking to have their charges or convictions overturned, saying they were based on unreliable evidence.
The county was re-accredited beginning in 2013, and most recently again this month, officials said.
At the new facility, Buffolino said, officials hope to reduce the average evidence analysis time. The lab processes evidence for the county's police departments, the district attorney's office and the medical examiner. While some of the work is already underway in Westbury, the full changeover is expected in the summer. There are 40 employees in East Meadow, and officials plan to add 10 to 15 more staffers.
"The end of June can't come soon enough," said Walker (R-Hicksville).


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