Learning Optimal Fair Policies

By Razieh Nabi, Daniel Malinsky, Ilya Shpitser. 2018

107 citations recorded by the source corpus.

Systematic discriminatory biases present in our society influence the way data is collected and stored, the way variables are defined, and the way scientific findings are put into practice as policy. Automated decision procedures and learning algorithms applied to such data may serve to perpetuate existing injustice or unfairness in our society. In this paper, we consider how to make optimal but fair decisions, which “break the cycle of injustice” by correcting for the unfair dependence of both decisions and outcomes on sensitive features (e.g., variables that correspond to gender, race, disability, or other protected attributes). We use methods from causal inference and constrained optimization to learn optimal policies in a way that addresses multiple potential biases which afflict data analysis in sensitive contexts, extending the approach of Nabi & Shpitser (2018). Our proposal comes equipped with the theoretical guarantee that the chosen fair policy will induce a joint distribution for new instances that satisfies given fairness constraints. We illustrate our approach with both synthetic data and real criminal justice data.

Subfields: Management Science and Operations Research, Safety Research, Reinforcement Learning, AI Ethics, Fairness & Society

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Citations in the corpus

A Survey on Bias and Fairness in Machine Learning (2019, methodology): “In [94] authors extended a formalization of algorithmic fairness from their previous work to the setting of learning optimal policies under fairness constraints.”
On the Fairness of Causal Algorithmic Recourse (2020, background): “…a protected attribute on the prediction, thus making fairness a fundamentally causal problem (Kilbertus et al. 2017; Russell et al. 2017; Loftus et al. 2018; Zhang and Bareinboim 2018a,b; Nabi and Shpitser 2018; Nabi, Malinsky, and Shpitser 2019; Chiappa 2019; Salimi et al. 2019; Wu et al. 2019).”
Fairness in Machine Learning (2020, background): “…135, 183, 185] or add penalties to the objective [5, 14, 22, 44, 46, 56, 58, 62, 73, 74, 75, 79, 82, 87, 88, 89, 93, 94, 95, 98, 104, 115, 119, 123, 133, 134, 135, 138, 139, 154, 165, 176, 179, 180, 182], others use adversary techniques to maximize the system ability to predict the target while…”
Agent Incentives: A Causal Perspective (2021, background): “Nabi, Malinsky, and Shpitser (2019) have shown how a policy may be chosen subject to path-specific effect constraints.”
Fairness in Machine Learning: A Survey (2020, background): “[5, 57, 65, 124, 195, 209, 210, 288, 112, 208, 75, 284, 164]) have similar objectives to fairness regularization approaches, and hence we present them together.”
Making Decisions that Reduce Discriminatory Impacts (2019, unknown): “…Heidari et al. (2019), who use a social dynamics model to create an ‘impacted dataset’ which represents how individuals respond to an algorithm, and (Nabi et al., 2019), who design policies using Q-learning and value search to make certain causal paths give the same impact across different…”