Towards trustworthy medical AI ecosystems – a proposal for supporting responsible innovation practices in AI-based medical innovation

Herzog, Christian; Blank, Sabrina; Stahl, Bernd Carsten

Abstract

In this article, we explore questions about the culture of trustworthy artificial intelligence (AI) through the lens of ecosystems. We draw on the European Commission’s Guidelines for Trustworthy AI and its philosophical underpinnings. Based on the latter, the trustworthiness of an AI ecosystem can be conceived of as being grounded by both the so-called rational-choice and motivation-attributing accounts—i.e., trusting is rational because solution providers deliver expected services reliably, while trust also involves resigning control by attributing one’s motivation, and hence, goals, onto another entity. Our research question is: What aspects contribute to a responsible AI ecosystem that can promote justifiable trustworthiness in a healthcare environment? We argue that especially within devising governance and support aspects of a medical AI ecosystem, considering the so-called motivation-attributing account of trust provides fruitful pointers. There can and should be specific ways and governance structures supporting and nurturing trustworthiness beyond mere reliability. After compiling a list of preliminary requirements for this, we describe the emergence of one particular medical AI ecosystem and assess its compliance with and future ways of improving its functioning as a responsible AI ecosystem that promotes trustworthiness.

Citation

Herzog, C., Blank, S., & Stahl, B. C. (2024). Towards trustworthy medical AI ecosystems – a proposal for supporting responsible innovation practices in AI-based medical innovation. AI & Society, https://doi.org/10.1007/s00146-024-02082-z

• Journal Article Type: Article
• Acceptance Date: Sep 11, 2024
• Online Publication Date: Oct 16, 2024
• Publication Date: Oct 16, 2024
• Deposit Date: Oct 22, 2024
• Publicly Available Date: Oct 23, 2024
• Journal: AI & SOCIETY
• Print ISSN: 0951-5666
• Electronic ISSN: 1435-5655
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1007/s00146-024-02082-z
• Public URL: https://nottingham-repository.worktribe.com/output/40705653
• Publisher URL: https://link.springer.com/article/10.1007/s00146-024-02082-z