Causal Equal Protection as Algorithmic Fairness
This addresses fairness concerns in algorithmic decision-making, particularly in legal contexts like trial proceedings, but appears incremental as it builds on existing causal fairness literature.
The paper tackles the problem of algorithmic fairness in classification systems by proposing a novel principle called causal equal protection, which combines classification parity with causal analysis to ensure individuals aren't subjected to uneven risks of classification error due to protected characteristics.
By combining the philosophical literature on statistical evidence and the interdisciplinary literature on algorithmic fairness, we revisit recent objections against classification parity in light of causal analyses of algorithmic fairness and the distinction between predictive and diagnostic evidence. We focus on trial proceedings as a black-box classification algorithm in which defendants are sorted into two groups by convicting or acquitting them. We defend a novel principle, causal equal protection, that combines classification parity with the causal approach. In the do-calculus, causal equal protection requires that individuals should not be subject to uneven risks of classification error because of their protected or socially salient characteristics. The explicit use of protected characteristics, however, may be required if it equalizes these risks.