arXiv:2605.01961v2 Announce Type: replace Abstract: Learning from human preference data is becoming a useful tool, from fine-tuning large language models to training reinforcement learning agents. However, in most scenarios, the model is trained on the average preference of all human evaluators, which, under large variations of preferences, can be unfair to minority groups. In this work, we consider fairness in dueling bandits, a standard framework for online learning from preference data. We assume that each user has a (potentially distinct) Condorcet winner, which is an arm preferred to ever
The increasing reliance on human preference data for AI model training, especially for large language models, necessitates robust methods for ensuring fairness amidst diverse user preferences.
This research addresses a critical fairness challenge in AI development, potentially leading to more ethical and equitably trained models that are less biased against minority preferences.
The proposed 'Multi-User Dueling Bandits' framework introduces a more sophisticated approach to incorporating diverse user preferences in AI training, moving beyond average-based approaches that can marginalize minorities.
- · AI ethicists and researchers
- · Users with minority preferences
- · Developers of preference-based AI systems
- · Companies seeking to reduce AI bias
- · Systems relying on simplistic preference aggregation
- · Models trained without fairness considerations
AI models will be trained with a more nuanced understanding of diverse human preferences.
Public trust and broader adoption of AI systems could improve as fairness concerns are addressed.
New regulatory frameworks for AI might incorporate requirements for fair preference learning, influencing development standards.
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Read at arXiv cs.LG