Measurable Trust: The Key to Unlocking User Confidence in Black-Box AI

Palazzolo, Puntis; Stahl, Bernd; Webb, Helena

Abstract

Given the pervasive integration of artificial intelligence (AI) into our daily lives, establishing public trust is paramount for maximizing AI's benefits and ensuring its responsible use. This research proposes an investigation into the feasibility of developing a globally accepted, context-specific "trustworthiness score" for AI systems. We recognize that trust is a dynamic construct influenced by individual experiences, situational factors, and inherent user characteristics. We hypothesize that by quantifying behavioral manifestations of trust, such as user acceptance and confidence level during interactions, as well as incorporating expert assessments and ethical considerations, we can indirectly measure AI trustworthiness. This approach aims to create a standardized framework that can guide responsible AI development, mitigate potential risks, and empower users to make informed decisions about trusting AI systems. The proposed research is particularly relevant in high-stakes sectors like healthcare and finance where AI decisions can significantly impact individuals and society, underscoring the need for transparency, accountability, and robust mechanisms to evaluate and build trust in AI technologies.

Citation

Palazzolo, P., Stahl, B., & Webb, H. (2024, September). Measurable Trust: The Key to Unlocking User Confidence in Black-Box AI. Presented at Second International Symposium on Trustworthy Autonomous Systems, Austin, Texas, USA

• Presentation Conference Type: Edited Proceedings
• Conference Name: Second International Symposium on Trustworthy Autonomous Systems
• Start Date: Sep 16, 2024
• End Date: Sep 18, 2024
• Acceptance Date: Sep 16, 2024
• Online Publication Date: Sep 16, 2024
• Publication Date: Sep 16, 2024
• Deposit Date: Sep 27, 2024
• Publicly Available Date: Oct 1, 2024
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Article Number: 18
• Pages: 1-7
• Book Title: TAS '24: Proceedings of the Second International Symposium on Trustworthy Autonomous Systems
• ISBN: 9798400709890
• DOI: https://doi.org/10.1145/3686038.3686058
• Public URL: https://nottingham-repository.worktribe.com/output/39729657
• Publisher URL: https://dl.acm.org/doi/10.1145/3686038.3686058