The fairness-accuracy landscape of neural classifiers
That machine learning algorithms can demonstrate bias is well-documented by now. This work confronts the challenge of bias mitigation in feedforward fully-connected neural nets from the lens of causal inference and multiobjective optimisation. Regarding the former, a new causal notion of fairness is introduced that is particularly suited to giving a nuanced treatment of datasets collected under unfair practices. In particular, special attention is paid to subjects whose covariates could appear with substantial probability in either value of the sensitive attribute. Next, recognising that fairness and accuracy are competing objectives, the proposed methodology uses techniques from multiobjective optimisation to ascertain the fairness-accuracy landscape of a neural net classifier. Experimental results suggest that the proposed method produces neural net classifiers that distribute evenly across the Pareto front of the fairness-accuracy space and is more efficient at finding non-dominated points than an adversarial approach.
The authors propose a novel joint optimisation framework that attempts to optimally trade-off between accuracy and fairness objectives, since in its general formal counterfactual fairness is at odds with classical accuracy objective. To this end, the authors propose optimising a Pareto front of the fairness-accuracy trade-off space and show that their resulting method outperforms an adversarial approach in finding non-dominated pairs. As main contributions, the paper provides: * A Pareto objective formulation of the accuracy fairness trade-off * A new causal fairness objective based on the existing Weighted Average Treatment Effect (WATE) and Average Treatment Effect for the Overlap Population (ATO) Overall, I think the paper makes an interesting contribution to the field of fairness and that the resulting method seems quite attractive for a real-world practitioners. However, I found the writing / notation imprecise at times and the experimental section too small (lacking an extensive set of baselines, and only on two datasets). For these reasons, I give it a Weak Accept. Some feedback on notation / writing: * Typo on page 2, the loss L should be defined on X x Y and not Y x Y * In page 5, h is being used without being introduced first * the justification for using ATO in the internal layers of the network is a bit insufficient In terms of suggestions, I think the experimental section needs to be extended and that the various modelling choices need to be explored and/or be further justified.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
The fairness-accuracy landscape of neural classifiers
That machine learning algorithms can demonstrate bias is well-documented by now. This work confronts the challenge of bias mitigation in feedforward fully-connected neural nets from the lens of causal inference and multiobjective optimisation. Regarding the former, a new causal notion of fairness is introduced that is particularly suited to giving a nuanced treatment of datasets collected under unfair practices. In particular, special attention is paid to subjects whose covariates could appear with substantial probability in either value of the sensitive attribute. Next, recognising that fairness and accuracy are competing objectives, the proposed methodology uses techniques from multiobjective optimisation to ascertain the fairness-accuracy landscape of a neural net classifier. Experimental results suggest that the proposed method produces neural net classifiers that distribute evenly across the Pareto front of the fairness-accuracy space and is more efficient at finding non-dominated points than an adversarial approach.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The authors propose a novel joint optimisation framework that attempts to optimally trade-off between accuracy and fairness objectives, since in its general formal counterfactual fairness is at odds with classical accuracy objective. To this end, the authors propose optimising a Pareto front of the fairness-accuracy trade-off space and show that their resulting method outperforms an adversarial approach in finding non-dominated pairs. As main contributions, the paper provides: * A Pareto objective formulation of the accuracy fairness trade-off * A new causal fairness objective based on the existing Weighted Average Treatment Effect (WATE) and Average Treatment Effect for the Overlap Population (ATO) Overall, I think the paper makes an interesting contribution to the field of fairness and that the resulting method seems quite attractive for a real-world practitioners. However, I found the writing / notation imprecise at times and the experimental section too small (lacking an extensive set of baselines, and only on two datasets). For these reasons, I give it a Weak Accept. Some feedback on notation / writing: * Typo on page 2, the loss L should be defined on X x Y and not Y x Y * In page 5, h is being used without being introduced first * the justification for using ATO in the internal layers of the network is a bit insufficient In terms of suggestions, I think the experimental section needs to be extended and that the various modelling choices need to be explored and/or be further justified.
assistant
The authors propose a novel joint optimisation framework that attempts to optimally trade-off between accuracy and fairness objectives, since in its general formal counterfactual fairness is at odds with classical accuracy objective. To this end, the authors propose optimising a Pareto front of the fairness-accuracy trade-off space and show that their resulting method outperforms an adversarial approach in finding non-dominated pairs. As main contributions, the paper provides: * A Pareto objective formulation of the accuracy fairness trade-off * A new causal fairness objective based on the existing Weighted Average Treatment Effect (WATE) and Average Treatment Effect for the Overlap Population (ATO) Overall, I think the paper makes an interesting contribution to the field of fairness and that the resulting method seems quite attractive for a real-world practitioners. However, I found the writing / notation imprecise at times and the experimental section too small (lacking an extensive set of baselines, and only on two datasets). For these reasons, I give it a Weak Accept. Some feedback on notation / writing: * Typo on page 2, the loss L should be defined on X x Y and not Y x Y * In page 5, h is being used without being introduced first * the justification for using ATO in the internal layers of the network is a bit insufficient In terms of suggestions, I think the experimental section needs to be extended and that the various modelling choices need to be explored and/or be further justified.