Multi Scale Ethics—Why We Need to Consider the Ethics of AI in Healthcare at Different Scales

Authors: Melanie Smallman (2022)

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Abstract: Many researchers have documented how AI and data driven technologies have the potential to have profound effects on our lives—in ways that make these technologies stand out from those that went before. Around the world, we are seeing a significant growth in interest and investment in AI in healthcare. This has been coupled with rising concerns about the ethical implications of these technologies and an array of ethical guidelines for the use of AI and data in healthcare has arisen. Nevertheless, the question of if and how AI and data technologies can be ethical remains open to debate. This paper aims to contribute to this debate by considering the wide range of implications that have been attributed to these technologies and asking whether current ethical guidelines take these factors into account. In particular, the paper argues that while current ethics guidelines for AI in healthcare effectively account for the four key issues identified in the ethics literature (transparency; fairness; responsibility and privacy), they have largely neglected wider issues relating to the way in which these technologies shape institutional and social arrangements. This, I argue, has given current ethics guidelines a strong focus on evaluating the impact of these technologies on the individual, while not accounting for the powerful social shaping effects of these technologies. To address this, the paper proposes a Multiscale Ethics Framework, which aims to help technology developers and ethical evaluations to consider the wider implications of these technologies.

Presenter: Kaylin Bolt

Date: 2023-01-31 15:00 (CET)