Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness

• URL: http://arxiv.org/abs/2310.19691v1
• Date: Mon, 30 Oct 2023 16:07:57 GMT
• Title: Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness
• Authors: Jacy Reese Anthis and Victor Veitch
• Abstract summary: We motivatefactual fairness by showing that there is not a fundamental trade-off between fairness and accuracy. Counterfactual fairness can sometimes be tested by measuring relatively simple group fairness metrics.
• Score: 15.83823345486604
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Counterfactual fairness requires that a person would have been classified in the same way by an AI or other algorithmic system if they had a different protected class, such as a different race or gender. This is an intuitive standard, as reflected in the U.S. legal system, but its use is limited because counterfactuals cannot be directly observed in real-world data. On the other hand, group fairness metrics (e.g., demographic parity or equalized odds) are less intuitive but more readily observed. In this paper, we use $\textit{causal context}$ to bridge the gaps between counterfactual fairness, robust prediction, and group fairness. First, we motivate counterfactual fairness by showing that there is not necessarily a fundamental trade-off between fairness and accuracy because, under plausible conditions, the counterfactually fair predictor is in fact accuracy-optimal in an unbiased target distribution. Second, we develop a correspondence between the causal graph of the data-generating process and which, if any, group fairness metrics are equivalent to counterfactual fairness. Third, we show that in three common fairness contexts$\unicode{x2013}$measurement error, selection on label, and selection on predictors$\unicode{x2013}$counterfactual fairness is equivalent to demographic parity, equalized odds, and calibration, respectively. Counterfactual fairness can sometimes be tested by measuring relatively simple group fairness metrics.

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