Abstract
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.
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