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Beyond ideals: why the (medical) AI industry needs to motivate behavioural change in line with fairness and transparency values, and how it can do it

Alice Liefgreen, Netta Weinstein, Sandra Wachter, Brent Mittelstadt

AI and SOCIETY

Swansea University Author: Alice Liefgreen

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DOI (Published version): 10.1007/s00146-023-01684-3

Abstract

Artificial intelligence (AI) is increasingly relied upon by clinicians for making diagnostic and treatment decisions, playing an important role in imaging, diagnosis, risk analysis, lifestyle monitoring, and health information management. While research has identified biases in healthcare AI systems...

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Published in: AI and SOCIETY
ISSN: 0951-5666 1435-5655
Published: Springer Science and Business Media LLC 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa63376
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Abstract: Artificial intelligence (AI) is increasingly relied upon by clinicians for making diagnostic and treatment decisions, playing an important role in imaging, diagnosis, risk analysis, lifestyle monitoring, and health information management. While research has identified biases in healthcare AI systems and proposed technical solutions to address these, we argue that effective solutions require human engagement. Furthermore, there is a lack of research on how to motivate the adoption of these solutions and promote investment in designing AI systems that align with values such as transparency and fairness from the outset. Drawing on insights from psychological theories, we assert the need to understand the values that underlie decisions made by individuals involved in creating and deploying AI systems. We describe how this understanding can be leveraged to increase engagement with de-biasing and fairness-enhancing practices within the AI healthcare industry, ultimately leading to sustained behavioral change via autonomy-supportive communication strategies rooted in motivational and social psychology theories. In developing these pathways to engagement, we consider the norms and needs that govern the AI healthcare domain, and we evaluate incentives for maintaining the status quo against economic, legal, and social incentives for behavior change in line with transparency and fairness values.
Keywords: Artificial intelligence, Healthcare, Medicine, Fairness, Bias, Motivation, Behaviour change
College: Faculty of Humanities and Social Sciences
Funders: Wellcome Trust ( 223765/Z/21/Z), Alfred P. Sloan Foundation (G-2021-16779), Department of Health and Social Care, British Academy ( PF2\180114), Luminate Group, Miami Foundation

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