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About

Yifang Chen is a researcher working at the intersection of human-robot interaction, algorithmic fairness, and sequential decision-making. Their work addresses a deceptively complex challenge: how should AI systems and robots fairly distribute resources among multiple human collaborators? Drawing on multi-armed bandit frameworks and reinforcement learning, Chen has developed theoretical and experimental methods that embed fairness constraints directly into resource allocation algorithms, ensuring that robotic agents do not inadvertently favor certain team members over others. Chen's most influential contribution, "Multi-Armed Bandits with Fairness Constraints for Distributing Resources to Human Teammates," has accumulated 30 citations since 2020, reflecting growing interest in ethical AI design for collaborative environments. Complementary work on fair contextual bandits extends these ideas to settings where an AI must adapt its decisions based on contextual information while still respecting fairness principles. Their 2019 reinforcement learning paper further demonstrates how these constraints can be applied dynamically in human-robot teams. Collectively, Chen's research highlights that optimal performance and equitable treatment need not be mutually exclusive goals—a timely and important message as AI systems become increasingly embedded in collaborative human workplaces and social settings.