Core Concepts
The core message of this article is to propose a process framework for understanding and managing trust in collaborative human-AI (HAI) teams. The framework, called CHAI-T, incorporates the context-specificity, team processes, and temporal dynamics that characterize trust development and maintenance in HAI teaming.
Abstract
The article presents an overview of current models of interpersonal trust and trust in artificial intelligence (AI), with the goal of identifying factors that are likely to be relevant in the context of collaborative HAI teaming. It then draws on these factors to propose the CHAI-T framework, which has the following key features:
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Context Specificity:
- The framework recognizes that factors influencing trust formation may differ qualitatively and quantitatively between different types of AI systems.
- It recommends profiling the specific collaborative HAI team and task environment to identify relevant trust antecedents.
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Team Processes:
- The framework incorporates team processes, such as monitoring progress, coordination, and communication, that may influence trust development and maintenance.
- It suggests that while some human teaming processes may be applicable, others may need to be adapted or replaced for collaborative HAI teams.
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Temporal Dynamics:
- The framework captures the dynamic nature of trust by incorporating recurring performance episodes, where outputs from one episode become inputs for the next.
- This allows for the identification of factors that increase or decrease trust over time, enabling active trust management.
The article discusses how the CHAI-T framework can be used to specify and validate models of trust for particular collaborative AI applications, and outlines future research directions, including the need for rigorous methods to measure trust and its temporal phenomena.