Collaborative Human-AI Trust (CHAI-T): A Process Framework for Active Management of Trust in Human-AI Collaboration

Temel Kavramlar

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.

Özet

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:

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.

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.

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.

İstatistikler

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Alıntılar

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Önemli Bilgiler Şuradan Elde Edildi

Collaborative human-AI trust (CHAI-T)

by Melanie J. M... : arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01615.pdf

Daha Derin Sorular

How can the CHAI-T framework be adapted to account for the unique characteristics of different types of collaborative AI systems (e.g., embodied robots vs. disembodied algorithms)?

The CHAI-T framework can be adapted to account for the unique characteristics of different types of collaborative AI systems by customizing the inputs, team processes, and temporal dynamics based on the specific attributes of each system. For embodied robots, factors such as physical presence, anthropomorphism, and non-verbal communication may play a significant role in trust development. Therefore, the framework can incorporate these factors as trust antecedents and team processes. On the other hand, disembodied algorithms may rely more on performance, transparency in decision-making, and communication clarity. Thus, the framework can be adjusted to emphasize these aspects in the trust model. By tailoring the framework to the specific characteristics of each type of collaborative AI system, researchers can ensure that the trust model is relevant and effective in guiding human-AI interaction.

What are the potential challenges in validating the CHAI-T framework and the specific trust models it informs, and how can these challenges be addressed?

One potential challenge in validating the CHAI-T framework and the specific trust models it informs is the complexity of measuring trust in human-AI collaboration. Trust is a multifaceted construct influenced by various factors, making it challenging to capture accurately. To address this challenge, researchers can employ a combination of quantitative and qualitative methods, such as surveys, behavioral observations, and interviews, to gather comprehensive data on trust development and dynamics. Additionally, establishing psychometrically validated trust measurement tools and conducting multi-method approaches can enhance the reliability and validity of the trust models derived from the framework.

How might the CHAI-T framework inform the design of collaborative workflows, system capabilities, and human training to support the calibration of trust in human-AI collaboration?

The CHAI-T framework can inform the design of collaborative workflows by identifying key team processes that influence trust development and maintenance. By understanding how factors such as communication, transparency, and coordination impact trust, designers can optimize workflow structures to enhance trust in human-AI collaboration. Moreover, the framework can guide the development of system capabilities by highlighting the importance of features like explainability, adaptability, and reliability in fostering trust. Human training programs can also be tailored based on the trust models derived from the framework, focusing on skills such as effective communication with AI systems, interpreting system feedback, and calibrating trust levels based on performance. Overall, the CHAI-T framework provides a structured approach to enhancing trust calibration in human-AI collaboration through informed design and training strategies.