Understanding human-machine team communication from an explainable-AI perspective

Roig Vilamala, Marc, Furby, Jack, de Gortari Briseno, Julian, Srivastava, Mani, Preece, Alun ORCID: https://orcid.org/0000-0003-0349-9057 and Fuentes, Carolina ORCID: https://orcid.org/0000-0002-0871-939X 2025. Understanding human-machine team communication from an explainable-AI perspective. Presented at: 34th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Eindhoven, Netherlands, 25-29 August 2025. 2025 34th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 402-409. 10.1109/ro-man63969.2025.11217893

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Abstract

In this paper, we explore how humans communicate with teammates from an explainable-AI perspective, comparing how they interact with both human and AI-controlled robot teammates under a number of different strategies in scenarios such as disaster relief. We find that while humans do adapt their communication based on which strategy they are following, they consistently communicate differently with AI teammates than human teammates, tending to give explicit orders to the former while sending more vague messages with implicit understanding to the later. However, we also find that modern Large Language Models (LLMs) are capable of understanding the explainability intent of messages to the same level as humans, implying that such differences may not be required if LLMs are fully integrated into AI agents.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	IEEE
ISBN:	9798331587727
Last Modified:	18 Nov 2025 10:31
URI:	https://orca.cardiff.ac.uk/id/eprint/182486

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