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Testing the Fairness-Improvability of Algorithms
In this paper, we provide an econometric framework for testing the hypothesis that it is possible to improve on the fairness of an algorithm without compromising on other pre-specified objectives.
Adaptive Experimental Design for Policy Learning
Setting the worst-case expected regret as the performance criterion of adaptive sampling and recommended policies, we derive its asymptotic lower bounds, and propose a strategy, Adaptive Sampling-Policy Learning strategy (PLAS), whose leading factor of the regret upper bound aligns with the lower bound as the size of experimental units increases.
Counterfactual Learning with General Data-generating Policies
Off-policy evaluation (OPE) attempts to predict the performance of counterfactual policies using log data from a different policy.
Algorithm Design: A Fairness-Accuracy Frontier
Whether it is optimal to ban an input generally depends on the designer's preferences.
