Counterfactual Fairness Is Basically Demographic Parity
Making fair decisions is crucial to ethically implementing machine learning algorithms in social settings. In this work, we consider the celebrated definition of counterfactual fairness [Kusner et al., NeurIPS, 2017]. We begin by showing that an algorithm which satisfies counterfactual fairness also satisfies demographic parity, a far simpler fairness constraint. Similarly, we show that all algorithms satisfying demographic parity can be trivially modified to satisfy counterfactual fairness. Together, our results indicate that counterfactual fairness is basically equivalent to demographic parity, which has important implications for the growing body of work on counterfactual fairness. We then validate our theoretical findings empirically, analyzing three existing algorithms for counterfactual fairness against three simple benchmarks. We find that two simple benchmark algorithms outperform all three existing algorithms -- in terms of fairness, accuracy, and efficiency -- on several data sets. Our analysis leads us to formalize a concrete fairness goal: to preserve the order of individuals within protected groups. We believe transparency around the ordering of individuals within protected groups makes fair algorithms more trustworthy. By design, the two simple benchmark algorithms satisfy this goal while the existing algorithms for counterfactual fairness do not.
Code (0)
등록된 구현이 없습니다.
Tasks
counterfactualFairnessSimilar Papers 제목 키워드 기반
Counterfactual Fairness Is Not Demographic Parity, and Other Observations
Blanket statements of equivalence between causal concepts and purely probabilistic concepts should be approached with care. In this short note, I examine a recent claim that counterfactual fairness is equivalent to demog…
counterfactualFairnessCausal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness
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 intui…
counterfactualFairnessFairness Through Counterfactual Utilities
Group fairness definitions such as Demographic Parity and Equal Opportunity make assumptions about the underlying decision-problem that restrict them to classification problems. Prior work has translated these definition…
BIG-bench Machine LearningcounterfactualFairnessreinforcement-learning+2FairLogue: A Toolkit for Intersectional Fairness Analysis in Clinical Machine Learning Models
Objective: Algorithmic fairness is essential for equitable and trustworthy machine learning in healthcare. Most fairness tools emphasize single-axis demographic comparisons and may miss compounded disparities affecting i…
Counterfactually Fair Regression via Optimal Transport
We consider the problem of learning a counterfactually fair regressor. We adopt a causal uncertainty view in which counterfactual fairness is defined with resampled noise. We focus on obtaining theoretical fairness guara…