Conflict-Driven Debiasing: Synthesizing Contradictory Fairness Metrics for Robust LLM Auditing

Fairness auditing often reveals tensions: a model may score well on Demographic Parity but poorly on Equal Opportunity (see BEATS, Abhishek et al., 2025; Chakraborty et al., 2025). Instead of treating these metric conflicts as a nuisance, this research asks: What can we learn by systematically analyzing and synthesizing these conflicts? Building on the “generate theories from conflict” heuristic, the project proposes a meta-auditing framework that mines discrepancies among fairness metrics across tasks, languages, and contexts. By modeling these conflicts, the framework could recommend targeted debiasing interventions (e.g., data augmentation, causal prompts, calibration) that explicitly address the observed trade-offs. This approach could yield new composite fairness objectives and highlight contexts where single-metric evaluations are dangerously misleading. The work would extend survey findings (Chu et al., 2024) and recent debiasing experiments (Li et al., 2024), driving the field toward more nuanced and holistic LLM fairness standards.

References:

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