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1. Group-Fair Classification with Strategic Agents

   https://doi.org/10.1145/3593013.3594006  

   Estornell, Andrew; Das, Sanmay; Liu, Yang; Vorobeychik, Yevgeniy (January 2023, ACM Conference on Fairness, Accountability, and Transparency)

   The use of algorithmic decision making systems in domains which impact the financial, social, and political well-being of people has created a demand for these to be “fair” under some accepted notion of equity. This demand has in turn inspired a large body of work focused on the development of fair learning algorithms which are then used in lieu of their conventional counterparts. Most analysis of such fair algorithms proceeds from the assumption that the people affected by the algorithmic decisions are represented as immutable feature vectors. However, strategic agents may possess both the ability and the incentive to manipulate this observed feature vector in order to attain a more favorable outcome. We explore the impact that strategic agent behavior can have on group-fair classification. We find that in many settings strategic behavior can lead to fairness reversal, with a conventional classifier exhibiting higher fairness than a classifier trained to satisfy group fairness. Further, we show that fairness reversal occurs as a result of a group- fair classifier becoming more selective, achieving fairness largely by excluding individuals from the advantaged group. In contrast, if group fairness is achieved by the classifier becoming more inclusive, fairness reversal does not occur. 

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2. Fairness Transferability Subject to Bounded Distribution Shift

   Chen, Yatong; Raab, Reilly; Wang, Jialu; Liu, Yang (January 2022, Neural Information Processing Systems (NeurIPS))

   Given an algorithmic predictor that is "fair" on some source distribution, will it still be fair on an unknown target distribution that differs from the source within some bound? In this paper, we study the transferability of statistical group fairness for machine learning predictors (i.e., classifiers or regressors) subject to bounded distribution shifts. Such shifts may be introduced by initial training data uncertainties, user adaptation to a deployed predictor, dynamic environments, or the use of pre-trained models in new settings. Herein, we develop a bound that characterizes such transferability, flagging potentially inappropriate deployments of machine learning for socially consequential tasks. We first develop a framework for bounding violations of statistical fairness subject to distribution shift, formulating a generic upper bound for transferred fairness violations as our primary result. We then develop bounds for specific worked examples, focusing on two commonly used fairness definitions (i.e., demographic parity and equalized odds) and two classes of distribution shift (i.e., covariate shift and label shift). Finally, we compare our theoretical bounds to deterministic models of distribution shift and against real-world data, finding that we are able to estimate fairness violation bounds in practice, even when simplifying assumptions are only approximately satisfied. 

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3. Fairness Under Composition

   Dwork, C; Ilvento, C (January 2019, 10th Innovations in Theoretical Computer Science Conference (ITCS 2019))

   Blum, A (Ed.)

   Algorithmic fairness, and in particular the fairness of scoring and classification algorithms, has become a topic of increasing social concern and has recently witnessed an explosion of research in theoretical computer science, machine learning, statistics, the social sciences, and law. Much of the literature considers the case of a single classifier (or scoring function) used once, in isolation. In this work, we initiate the study of the fairness properties of systems composed of algorithms that are fair in isolation; that is, we study fairness under composition. We identify pitfalls of naïve composition and give general constructions for fair composition, demonstrating both that classifiers that are fair in isolation do not necessarily compose into fair systems and also that seemingly unfair components may be carefully combined to construct fair systems. We focus primarily on the individual fairness setting proposed in [Dwork, Hardt, Pitassi, Reingold, Zemel, 2011], but also extend our results to a large class of group fairness definitions popular in the recent literature, exhibiting several cases in which group fairness definitions give misleading signals under composition. 

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4. Popularizing Fairness: Group Fairness and Individual Welfare

   https://doi.org/10.1609/aaai.v37i6.25910  

   Estornell, Andrew; Das, Sanmay; Juba, Brendan; Vorobeychik, Yevgeniy (June 2023, Proceedings of the AAAI Conference on Artificial Intelligence)

   Group-fair learning methods typically seek to ensure that some measure of prediction efficacy for (often historically) disadvantaged minority groups is comparable to that for the majority of the population. When a principal seeks to adopt a group-fair approach to replace another, the principal may face opposition from those who feel they may be harmed by the switch, and this, in turn, may deter adoption. We propose that a potential mitigation to this concern is to ensure that a group-fair model is also popular, in the sense that, for a majority of the target population, it yields a preferred distribution over outcomes compared with the conventional model. In this paper, we show that state of the art fair learning approaches are often unpopular in this sense. We propose several efficient algorithms for postprocessing an existing group-fair learning scheme to improve its popularity while retaining fairness. Through extensive experiments, we demonstrate that the proposed postprocessing approaches are highly effective in practice. 

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5. Achieving Fair Treatment in Algorithmic Classification

   Andrew, Morgan (January 2018, Theory of Cryptography Conference)

   Fairness in classification has become an increasingly relevant and controversial issue as com- puters replace humans in many of today’s classification tasks. In particular, a subject of much recent debate is that of finding, and subsequently achieving, suitable definitions of fairness in an algorithmic context. In this work, following the work of Hardt et al. (NIPS’16), we consider and formalize the task of sanitizing an unfair classifier C into a classifier C′ satisfying an approximate notion of “equalized odds” or fair treatment. Our main result shows how to take any (possibly unfair) classifier C over a finite outcome space, and transform it—by just perturbing the out- put of C—according to some distribution learned by just having black-box access to samples of labeled, and previously classified, data, to produce a classifier C′ that satisfies fair treatment; we additionally show that our derived classifier is near-optimal in terms of accuracy. We also experimentally evaluate the performance of our method. 

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