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This paper investigates the impact of demographic bias (sex and age) on skin lesion classification using ResNet models, finding that sex biases stem from data imbalances while age biases consistently favor younger groups, and evaluating multi-task and adversarial learning mitigation strategies.
DART (Distill-Audit-Repair Training) is a new training framework that addresses 'harm drift' in safety-aligned LLMs, where fine-tuning for demographic difference-awareness causes harmful content to appear in model explanations. On eight benchmarks, DART improves Llama-3-8B-Instruct accuracy from 39.0% to 68.8% while reducing harm drift cases by 72.6%.