Yielding Fairness: Coupled Learning and Fairness for Value-Weighted Ranking

Ami Tavory, Yaniv Sheena, Shaked Bar, Yair Zagdansky

Risk screening and recommendation systems pass or block accounts that generate both value and loss. Under a blocked-value budget, a cap on expected value forgone through blocking, the optimal ranking orders accounts by expected value per unit expected loss, a ratio of conditional expectations. This rule can conflict with accepted harm or net-harm criteria, blocking accounts that another criterion would pass at the same budget. We call these cases account-level objections and define group complaint disparity as the difference between groups' normalized realized objections. Weighted binary cross-entropy (W-BCE) implements the value-per-loss order with one score. Objection penalties modify the ranking, while group multipliers scale those penalties differently by group, tracing tradeoffs between loss capture and complaint disparity. This preserves the value-budgeted ranking structure and makes the cost of reducing contestable blocks explicit. Across public recommendation, insurance, credit, and mortgage datasets, we compare undamped W-BCE with ordinary BCE using yield Gini, an area summary of loss capture versus blocked value. Logarithmic weight damping is evaluated separately.…