  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.
