Abstract: As data science has broadened its scope in recent years, a number of domains have applied computational methods for classification and prediction to evaluate individuals in high-stakes settings. These developments have led to an active line of recent discussion in the public sphere about the consequences of algorithmic prediction for notions of fairness and equity. In part, this discussion has involved a basic tension between competing notions of what it means for such classifications to be fair to different…