Bias Mitigation via Compensation: A Reinforcement Learning Perspective

arXiv:2404.19256v1 Announce Type: cross
Abstract: As AI increasingly integrates with human decision-making, we must carefully consider interactions between the two. In particular, current approaches focus on optimizing individual agent actions but often overlook the nuances of collective intelligence. Group dynamics might require that one agent (e.g., the AI system) compensate for biases and errors in another agent (e.g., the human), but this compensation should be carefully developed. We provide a theoretical framework for algorithmic compensation that synthesizes game theory and …

abstract agent ai system arxiv bias biases collective compensation cs.ai cs.cy cs.gt cs.hc cs.lg cs.ma current decision dynamics focus human intelligence interactions making perspective reinforcement reinforcement learning type via