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Explainability Analysis for Skill Execution

Authors:
Khatina Sari
Paul G. Plöger
Alex Mitrevski

Keywords: Artificial Intelligence; Natural Language Processing; Heatmaps; Human-Robot Interaction

Abstract:
Explainability holds significant importance for autonomous robots deployed in human-centered situations, particularly when errors occur during execution. In the context of robot action, it is important to consider various levels and types of explainability. The social dimension of Artificial Intelligence (AI) and robotic explanations, which highlights how they affect social interaction, values, and decision-making, has received little to no attention in prior research. With a particular emphasis on item handover, we hypothesize that users prefer systems with explanations and that explanations in natural language are more appealing than heatmaps. A user study, involving participants from diverse backgrounds and levels of expertise, is conducted to evaluate different levels and preferred types of explainability. The study results support our hypotheses and offer additional valuable information for future system development.

Pages: 14 to 20

Copyright: Copyright (c) IARIA, 2025

Publication date: October 26, 2025

Published in: conference

ISBN: 978-1-68558-318-7

Location: Barcelona, Spain

Dates: from October 26, 2025 to October 30, 2025