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. The key idea is 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. Furthermore, we explain how principal fairness differs from 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. We briefly discuss how to approach empirical evaluation and policy learning problems under the proposed principal fairness criterion.
Latest posts by Ryan Watkins (see all)
- On Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making - September 27, 2022
- Adverse effects of information personalization on human learning - September 26, 2022
- eXtended [Reality] Artificial Intelligence: New Prospects of Human-AI Interaction Research - September 19, 2022