Abstract
Applications of artificial intelligence/machine learning (AI/ML) in health care are dynamic and rapidly growing. One strategy for anticipating and addressing ethical challenges related to AI/ML for health care is patient and public involvement in the design of those technologies – often referred to as ‘co-design’. Co-design has a diverse intellectual and practical history, however, and has been conceptualized in many different ways. Moreover, AI/ML introduces challenges to co-design that are often underappreciated. Informed by perspectives from critical data studies and critical digital health studies, we review the research literature on involvement in health care, and involvement in design, and examine the extent to which co-design as commonly conceptualized is capable of addressing the range of normative issues raised by AI/ML for health care. We suggest that AI/ML technologies have amplified and modified existing challenges related to patient and public involvement, and created entirely new challenges. We outline three pitfalls associated with co-design for ethical AI/ML for health care and conclude with suggestions for addressing these practical and conceptual challenges.
Highlights
The contemporary field of artificial intelligence/machine learning (AI/ML) is dynamic and rapidly growing, characterized as central to a ‘4th industrial revolution’ that commentators suggest will impact virtually all aspects of our lives (Couldry and Mejias, 2019; Schwab, 2017; Zuboff, 2019)
AI/ML technologies are multi-purpose, they are consequential in health care, where concerns range from the changing nature of the patient–provider relationship (Goldhahn et al, 2018; Topol, 2019), to the ways in which AI/ML technologies exacerbate existing societal inequities (Benjamin, 2019; D’Ignazio and Klein, 2020; Eubanks, 2018; Noble, 2018)
Informed by perspectives from critical data studies (CDS; boyd and Crawford, 2012; Dalton and Thatcher, 2014; Kitchin and Lauriault, 2014) and critical digital health studies (CDHS; Lupton, 2016, 2017a), in this paper we outline three pitfalls associated with co-design for ethical AI/ML for health based on common assumptions arising from health care and co-design discourse
Summary
The contemporary field of artificial intelligence/machine learning (AI/ML) is dynamic and rapidly growing, characterized as central to a ‘4th industrial revolution’ that commentators suggest will impact virtually all aspects of our lives (Couldry and Mejias, 2019; Schwab, 2017; Zuboff, 2019). One strategy for anticipating and addressing the potential benefits and harms of AI/ML for health is patient and public involvement (PPI) in the design of those technologies, often referred to as co-design.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
