Author

Karthik Kamuju

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Preprint Aug 2026

Procedural Fairness Failures in RLHF from Preference Averaging

The results show that procedural fairness failures in alignment can arise from structural design choices in reward learning, even in controlled, noise-free settings, with direct implications for large language models and agentic systems, where biased reward models can compound inequities across sequential decisions.

M. Gopinadh, Karthik Kamuju, K. Avinash et al. · 0 citations