Kayla Boggess

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About

Kayla Boggess is a researcher at the forefront of human-robot interaction, specializing in making autonomous systems more transparent and trustworthy. Her primary research areas include explainable AI (XAI), robotic planning, and contrastive reasoning. Boggess’s most notable contribution is her pioneering work on contrastive explanations for robotic planning, as detailed in her highly cited paper "Towards Transparent Robotic Planning via Contrastive Explanations" (2020, 10 citations). This work draws on cognitive science insights to argue that the most effective explanations are not merely descriptive but contrastive—they clarify why a robot chose one action over another, thereby addressing users’ implicit comparisons and building deeper trust. By bridging social science theory with algorithmic design, Boggess has laid a critical foundation for developing robots that can articulate their decision-making processes in human-understandable terms. Her research is essential for the safe and ethical deployment of autonomous systems in sensitive domains like healthcare, autonomous driving, and collaborative manufacturing. Boggess’s work stands out for its interdisciplinary rigor and practical relevance, marking her as a rising leader in the quest for truly transparent and collaborative AI.