bias-amplification

This paper introduces the 'fairness collapse' phenomenon, showing that training language models on synthetic data silently amplifies social biases before standard model collapse metrics degrade, highlighting a critical risk for AI fairness.

This study reveals a 'Smart Pruning Paradox' where activation-aware pruning methods like Wanda preserve perplexity but significantly amplify bias in Large Language Models deployed on edge devices.