Responsibility and Regulation: Exploring Social Measures of Trust in Medical AI

McGarry, Glenn; Crabtree, Andrew; Chamberlain, Alan; Urquhart, Lachlan D

Abstract

This paper explores expert accounts of autonomous systems (AS) development in the medical device domain (MD) involving applications of artificial intelligence (AI), machine learning (ML), and other algorithmic and mathematical modelling techniques. We frame our observations with respect to notions of responsible innovation (RI) and the emerging problem of how to do RI in practice. In contribution to the ongoing discourse surrounding trustworthy autonomous system (TAS) [29], we illuminate practical challenges inherent in deploying novel AS within existing governance structures, including domain specific regulations and policies, and rigorous testing and development processes, and discuss the implications of these for the distribution of responsibility in novel AI deployment.

Citation

McGarry, G., Crabtree, A., Chamberlain, A., & Urquhart, L. D. (2024, September). Responsibility and Regulation: Exploring Social Measures of Trust in Medical AI. Presented at Second International Symposium on Trustworthy Autonomous Systems (TAS ’24), Austin, Texas, USA

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Second International Symposium on Trustworthy Autonomous Systems (TAS ’24)
• Start Date: Sep 16, 2024
• End Date: Sep 18, 2024
• Acceptance Date: Aug 1, 2024
• Online Publication Date: Sep 16, 2024
• Publication Date: Sep 16, 2024
• Deposit Date: Sep 20, 2024
• Publicly Available Date: Sep 16, 2024
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Article Number: 27
• Book Title: TAS '24: Proceedings of the Second International Symposium on Trustworthy Autonomous Systems
• ISBN: 9798400709890
• DOI: https://doi.org/10.1145/3686038.3686041
• Keywords: CCS CONCEPTS • Human-centered computing; • Human computer interac- tion (HCI); • Empirical studies in HCI; KEYWORDS Trustworthy Autonomous Systems, Responsible AI, Ethnography, Medical AI, SaMD
• Public URL: https://nottingham-repository.worktribe.com/output/38109223
• Publisher URL: https://dl.acm.org/doi/10.1145/3686038.3686041