Evaluation of Failure Communication Strategies for Trust Repair in Human-AI Collaboration

Abstract

The increasing application of Large Language Models (LLMs) in everyday tasks and at work highlights the crucial importance of trust in human-AI collaboration, particularly when an AI system fails. This paper investigates the effectiveness of failure communication strategies for trust repair in collaborative physical tasks involving a a chat-based AI assistant. A controlled experiment in which participants built LEGO cars guided by an LLM-based AI Assistant was used to evaluate whether findings from trust repair in a virtual environment, such as chatbots, translate to an environment comprising tangible tasks, and whether the timing of trust repair influences the outcome. Results indicate that actively communicating mistakes significantly improves trust compared to a no repair strategy, and that early repair tends to be more effective, indicating that failure communication, independent of the timing, is important for an appropriate calibration of trust.

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