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Using the concept of principal
stratification from the causal inference literature, we introduce a
new notion of fairness, called principal fairness, for human and
algorithmic decision-making. Principal fairness states that one
should not discriminate among individuals who would be similarly
affected by the decision. Unlike the existing statistical
definitions of fairness, principal fairness explicitly accounts for
the fact that individuals can be impacted by the decision. This
causal fairness formulation also enables online or post-hoc fairness
evaluation and policy learning. We also explain how principal
fairness relates to the existing causality-based fairness
criteria. In contrast to the counterfactual fairness criteria, for
example, principal fairness considers the effects of decision in
question rather than those of protected attributes of interest.
Finally, we discuss how to conduct empirical evaluation and policy
learning under the proposed principal fairness criterion.
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Imai, Kosuke, Zhichao Jiang, D. James
Greiner, Ryan Halen, and Sooahn Shin. ``Experimental Evaluation of Algorithm-Assisted
Human Decision-Making: Application to Pretrial Public Safety
Assessment.'' (with discussion) Journal of the
Royal Statistical Society, Series A (Statistics in Society),
Forthcoming. To be read before the Royal Statistical
Society. |
Ben-Michael, Eli, D. James Greiner, Melody
Huang, Kosuke Imai, Zhichao Jiang, Sooahn Shin. ``Does AI help humans make better decisions? A
statistical evaluation framework for experimental and observational
studies.'' |
Ben-Michael, Eli, D. James Greiner, Kosuke
Imai, and Zhichao Jiang. ``Safe
Policy Learning through Extrapolation: Application to Pre-trial
Risk Assessment.'' |
Ben-Michael, Eli, Kosuke Imai, and Zhichao
Jiang. ``Policy Learning with
Asymmetric Counterfactual Utilities.'' |
Koch, Benedikt and Kosuke Imai. ``Statistical Decision Theory with
Counterfactual Loss.'' |