Towards Balancing Preference and Performance through Adaptive Personalized Explainability

• URL: http://arxiv.org/abs/2504.13856v1
• Date: Fri, 21 Mar 2025 17:31:25 GMT
• Title: Towards Balancing Preference and Performance through Adaptive Personalized Explainability
• Authors: Andrew Silva, Pradyumna Tambwekar, Mariah Schrum, Matthew Gombolay,
• Abstract summary: We present two user-studies set in a simulated autonomous vehicle (AV) domain.<n>We investigate (1) population-level preferences for xAI and (2) personalization strategies for providing robot explanations.
• Score: 5.297303831218776
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As robots and digital assistants are deployed in the real world, these agents must be able to communicate their decision-making criteria to build trust, improve human-robot teaming, and enable collaboration. While the field of explainable artificial intelligence (xAI) has made great strides to enable such communication, these advances often assume that one xAI approach is ideally suited to each problem (e.g., decision trees to explain how to triage patients in an emergency or feature-importance maps to explain radiology reports). This fails to recognize that users have diverse experiences or preferences for interaction modalities. In this work, we present two user-studies set in a simulated autonomous vehicle (AV) domain. We investigate (1) population-level preferences for xAI and (2) personalization strategies for providing robot explanations. We find significant differences between xAI modes (language explanations, feature-importance maps, and decision trees) in both preference (p < 0.01) and performance (p < 0.05). We also observe that a participant's preferences do not always align with their performance, motivating our development of an adaptive personalization strategy to balance the two. We show that this strategy yields significant performance gains (p < 0.05), and we conclude with a discussion of our findings and implications for xAI in human-robot interactions.

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