3 papers
cs.MA2026
Procedural Fairness in Multi-Agent Bandits
Joshua Caiata, Carter Blair, Kate Larson
In the context of multi-agent multi-armed bandits (MA-MAB), fairness is often reduced to outcomes: maximizing welfare, reducing inequality, or balancing utilities. However, evidenc…
cs.AI2025
Generating Fair Consensus Statements with Social Choice on Token-Level MDPs
Carter Blair, Kate Larson
Current frameworks for consensus statement generation with large language models lack the inherent structure needed to provide provable fairness guarantees when aggregating diverse…
cs.AI2025
Reflective Verbal Reward Design for Pluralistic Alignment
Carter Blair, Kate Larson, Edith Law
AI agents are commonly aligned with "human values" through reinforcement learning from human feedback (RLHF), where a single reward model is learned from aggregated human feedback…