A new utility-based framework optimizes performance-fairness trade-offs in decisions by modeling decision-maker and decision-subject utilities and using a social planner's utility to capture group inequalities under different justice principles.
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Traditional ML bias mitigation methods outperform LLM-based methods in both fairness and predictive performance, with prior LLM advantages driven by artificially balanced test data rather than realistic imbalanced distributions.
citing papers explorer
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First-See-Then-Design: A Multi-Stakeholder View for Optimal Performance-Fairness Trade-Offs
A new utility-based framework optimizes performance-fairness trade-offs in decisions by modeling decision-maker and decision-subject utilities and using a social planner's utility to capture group inequalities under different justice principles.
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LLMs Are Not a Silver Bullet: A Case Study on Software Fairness
Traditional ML bias mitigation methods outperform LLM-based methods in both fairness and predictive performance, with prior LLM advantages driven by artificially balanced test data rather than realistic imbalanced distributions.