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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. Lookahead Counterfactual Fairness

   Zuo, Zhiqun; Xie, Tian; Tan, Xuwei; Zhang, Xueru; Khalili, Mohammad Mahdi (December 2024, Transactions on Machine Learning Research)

   As machine learning (ML) algorithms are used in applications that involve humans, concerns have arisen that these algorithms may be biased against certain social groups. Counterfactual fairness (CF) is a fairness notion proposed in Kusner et al. (2017) that measures the unfairness of ML predictions; it requires that the prediction perceived by an individual in the real world has the same marginal distribution as it would be in a counterfactual world, in which the individual belongs to a different group. Although CF ensures fair ML predictions, it fails to consider the downstream effects of ML predictions on individuals. Since humans are strategic and often adapt their behaviors in response to the ML system, predictions that satisfy CF may not lead to a fair future outcome for the individuals. In this paper, we introduce lookahead counterfactual fairness (LCF), a fairness notion accounting for the downstream effects of ML models which requires the individual future status to be counterfactually fair. We theoretically identify conditions under which LCF can be satisfied and propose an algorithm based on the theorems. We also extend the concept to path-dependent fairness. Experiments on both synthetic and real data validate the proposed method 

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3. A Canonical Data Transformation for Achieving Inter- and Within-Group Fairness

   https://doi.org/10.1109/TIFS.2024.3416040  

   Lazri, Zachary McBride; Brugere, Ivan; Tian, Xin; Dachman-Soled, Dana; Polychroniadou, Antigoni; Dervovic, Danial; Wu, Min (June 2024, IEEE Transactions on Information Forensics and Security)

   Increases in the deployment of machine learning algorithms for applications that deal with sensitive data have brought attention to the issue of fairness in machine learning. Many works have been devoted to applications that require different demographic groups to be treated fairly. However, algorithms that aim to satisfy inter-group fairness (also called group fairness) may inadvertently treat individuals within the same demographic group unfairly. To address this issue, this article introduces a formal definition of within-group fairness that maintains fairness among individuals from within the same group. A pre-processing framework is proposed to meet both inter- and within-group fairness criteria with little compromise in performance. The framework maps the feature vectors of members from different groups to an inter-group fair canonical domain before feeding them into a scoring function. The mapping is constructed to preserve the relative relationship between the scores obtained from the unprocessed feature vectors of individuals from the same demographic group, guaranteeing within-group fairness. This framework has been applied to the Adult, COMPAS risk assessment, and Law School datasets, and its performance is demonstrated and compared with two regularization-based methods in achieving inter-group and within-group fairness. 

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4. Attainability and Optimality: The Equalized Odds Fairness Revisited

   Tang, Zeyu; Zhang, Kun (April 2022, Proceedings of Machine Learning Research)

   Schölkopf, Bernhard; Uhler, Caroline; Zhang, Kun (Ed.)

   Fairness of machine learning algorithms has been of increasing interest. In order to suppress or eliminate discrimination in prediction, various notions as well as approaches have been proposed to impose fairness. Given a notion of fairness, an essential problem is then whether or not it can always be attained, even if with an unlimited amount of data. This issue is, however, not well addressed yet. In this paper, focusing on the Equalized Odds notion of fairness, we consider the attainability of this criterion and, furthermore, if it is attainable, the optimality of the prediction performance under various settings. In particular, for prediction performed by a deterministic function of input features, we give conditions under which Equalized Odds can hold true; if the stochastic prediction is acceptable, we show that under mild assumptions, fair predictors can always be derived. For classification, we further prove that compared to enforcing fairness by post-processing, one can always benefit from exploiting all available features during training and get potentially better prediction performance while remaining fair. Moreover, while stochastic prediction can attain Equalized Odds with theoretical guarantees, we also discuss its limitation and potential negative social impacts. 

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5. Interventional Fairness: Causal Database Repair for Algorithmic Fairness

   Salimi, Babak; Rodriguez, Luke; Howe, Bill; Suciu, Dan (April 2019, SIGMOD)

   Fairness is increasingly recognized as a critical component of machine learning systems. However, it is the underlying data on which these systems are trained that often reflect discrimination, suggesting a database repair problem. Existing treatments of fairness rely on statistical correlations that can be fooled by statistical anomalies, such as Simpson's paradox. Proposals for causality-based definitions of fairness can correctly model some of these situations, but they require specification of the underlying causal models. In this paper, we formalize the situation as a database repair problem, proving sufficient conditions for fair classifiers in terms of admissible variables as opposed to a complete causal model. We show that these conditions correctly capture subtle fairness violations. We then use these conditions as the basis for database repair algorithms that provide provable fairness guarantees about classifiers trained on their training labels. We evaluate our algorithms on real data, demonstrating improvement over the state of the art on multiple fairness metrics proposed in the literature while retaining high utility. 

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