Why Interpreting Intent Is Key for Trustworthiness in the Age of Opaque Agents

Victor Gimenez-Abalos; Luis Oliva-Felipe; Javier Vázquez-Salceda; Ulises Cortés; Sergio Alvarez-Napagao

doi:10.20944/preprints202402.1446.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 25 February 2024 / Approved: 26 February 2024 / Online: 26 February 2024 (12:12:43 CET)

This paper addresses the critical issue of trust in Artificial Intelligence systems, especially when users might find it challenging to comprehend the internal decision-making processes of such systems. A relevant topic of research in this respect is Theory of Mind, which involves attempting to understand these systems as if they possessed beliefs, desires, and intentions. We focus on the latter: intentions, and we examine how producing explanations based on them can improve \emph{understandability} while allowing for a better interpretation of how they align with human values. We also review some existing methods for identifying intentions in AI systems and we conclude with a discussion on possible future directions in this line of research.

explainability, intention inference, values alignment, multi-agent systems

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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Why Interpreting Intent Is Key for Trustworthiness in the Age of Opaque Agents

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