Equal Opportunity and Affirmative Action via Counterfactual Predictions
This addresses fairness in automated decision-making for domains like admissions and credit, but it is incremental as it builds on existing causal and fairness methods.
The paper tackles the problem of machine learning predictors inheriting discriminatory policies from historical data by proposing two algorithms that adjust predictors to satisfy equal opportunity and affirmative action fairness notions, proving theoretical optimality and evaluating trade-offs on datasets like admissions and income.
Machine learning (ML) can automate decision-making by learning to predict decisions from historical data. However, these predictors may inherit discriminatory policies from past decisions and reproduce unfair decisions. In this paper, we propose two algorithms that adjust fitted ML predictors to make them fair. We focus on two legal notions of fairness: (a) providing equal opportunity (EO) to individuals regardless of sensitive attributes and (b) repairing historical disadvantages through affirmative action (AA). More technically, we produce fair EO and AA predictors by positing a causal model and considering counterfactual decisions. We prove that the resulting predictors are theoretically optimal in predictive performance while satisfying fairness. We evaluate the algorithms, and the trade-offs between accuracy and fairness, on datasets about admissions, income, credit and recidivism.