Zhixuan Zhou

Papers

Total Citations

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About

Zhixuan Zhou is an emerging researcher whose work sits at the intersection of artificial intelligence ethics and machine learning systems. Their most significant contribution centers on empirically investigating real-world AI ethics failures, leveraging the AI Incident Database to systematically analyze how ethical principles break down in practice. This research, which has accumulated 50 combined citations across its iterations, addresses a critical gap between abstract AI ethics guidelines issued by governments and corporations and their tangible, on-the-ground effectiveness — arguing that the vagueness of existing frameworks contributes to their limited impact. By grounding ethics research in documented incidents rather than theoretical constructs, Zhou brings a data-driven rigor to a field often dominated by philosophical discourse. Beyond ethics, Zhou has also explored the technical frontiers of robotics, proposing an Accelerated Reward Policy (ARP) framework designed to improve the efficiency of deep reinforcement learning in robotic applications. Together, these contributions reflect a researcher who moves fluidly between normative questions about how AI *should* behave and technical questions about how AI *does* behave — a combination that positions Zhou as a thoughtful and versatile voice in responsible AI development.