Charlotte Young
Papers
2
Total Citations
14
H-Index
2
About
Charlotte Young is a rising researcher at the intersection of explainable artificial intelligence (XAI), human-robot interaction, and reinforcement learning. Her work addresses a critical challenge in modern AI: making autonomous systems transparent and trustworthy to human users. Young’s most-cited paper, "Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios" (2022), has accumulated 12 citations, establishing her as a thoughtful voice in the field. In this work, she investigates how robots can generate explanations that mimic human reasoning, moving beyond technical transparency to foster genuine user understanding and acceptance. By focusing on the quality and naturalness of explanations, Young’s research bridges the gap between complex decision-making algorithms and intuitive human communication. Her contributions are particularly relevant for safety-critical applications where human oversight is essential. As an early-career scholar, Young is helping to shape how we design robots that not only act intelligently but also explain their actions in ways people can trust and comprehend.
Research Focus
Key Achievements
Top Papers
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