Principles alone cannot guarantee ethical AI

Mittelstadt, Brent

doi:10.1038/s42256-019-0114-4

Cited by 659 publications

(517 citation statements)

References 59 publications

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“…While robo-advisors can often be accessed more easily now via smartphonessimilarly to any other mobile financial service (e.g., Luarn & Lin, 2005;Malaquias & Hwang, 2016;Yen & Wu, 2016) our findings suggest that many people might still be reluctant to use such services, due to machines' lack of legitimacy to make decisions with a moral component (Bigman et al, 2019;Bigman & Gray, 2018). An interesting issue is what degree of responsibility "financial machines" will actually bear in future financial crises (e.g., J.-W. Hong & Williams, 2019), which touches the issues of ethical AI (Mittelstadt, 2019).…”

Section: Discussionmentioning

confidence: 80%

Robo-investment aversion

Niszczota¹,

Kaszás²

2020

Preprint

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In three experiments (N = 2380), we show that people find it more permissible when investment decisions concerning controversial (“sin”) stocks are made by human fund managers rather than by computers (“robos”). In Study 1 (N = 466), participants rated the permissibility of a computer (algorithm) to autonomously exclude morally controversial stocks from investment portfolios as lower than if a human fund manager did the same; this finding was not different if participants were informed that such exclusions might be financially disadvantageous for them. In Study 2 (N = 1231), we show that this robo-fund aversion manifests itself both when considering investment in controversial and non-controversial industries. In Study 3 (N = 683), our findings show that robo-fund aversion is also present when algorithms are given the autonomy to increase investment in controversial stocks. Our findings suggest that algorithm aversion extends to the financial realm, supporting the existence of a barrier for the adoption of innovative financial technologies (FinTech), but one that might be overcome by hybrid (human-in-the-loop) solutions.

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Section: Discussionmentioning

confidence: 80%

Robo-investment aversion

Niszczota¹,

Kaszás²

2020

Preprint

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“…Such principles, for example, autonomy, justice or non-maleficence, can provide orientation. But they need to be embedded into a context-sensitive framework 31. On the other hand, patients’ openness to AI-driven health tools varies considerably across applications and countries 32.…”

Section: The Normative Challengesmentioning

confidence: 99%

Primer on an ethics of AI-based decision support systems in the clinic

Braun¹,

et al. 2020

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Making good decisions in extremely complex and difficult processes and situations has always been both a key task as well as a challenge in the clinic and has led to a large amount of clinical, legal and ethical routines, protocols and reflections in order to guarantee fair, participatory and up-to-date pathways for clinical decision-making. Nevertheless, the complexity of processes and physical phenomena, time as well as economic constraints and not least further endeavours as well as achievements in medicine and healthcare continuously raise the need to evaluate and to improve clinical decision-making. This article scrutinises if and how clinical decision-making processes are challenged by the rise of so-called artificial intelligence-driven decision support systems (AI-DSS). In a first step, this article analyses how the rise of AI-DSS will affect and transform the modes of interaction between different agents in the clinic. In a second step, we point out how these changing modes of interaction also imply shifts in the conditions of trustworthiness, epistemic challenges regarding transparency, the underlying normative concepts of agency and its embedding into concrete contexts of deployment and, finally, the consequences for (possible) ascriptions of responsibility. Third, we draw first conclusions for further steps regarding a ‘meaningful human control’ of clinical AI-DSS.

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“…Guidelines and frameworks are currently being developed to help ensure the accountable design, development, and use of digital health tools (Henson et al 2019;Torous et al 2019;Jessica Morley and Floridi 2019a). However, these developments will not easily transpose into non-clinical settings where comparable mechanisms of accountability and behavioural norms are lacking (Mittelstadt 2019). While there is, in principle, no a priori reason to doubt that such mechanisms could be established in non-clinical settings (e.g.…”

Section: Automating Interventionsmentioning

confidence: 99%

The Ethics of Digital Well-Being: A Multidisciplinary Perspective

Burr

Floridi

2020

Philosophical Studies Series

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This chapter serves as an introduction to the edited collection of the same name, which includes chapters that explore digital well-being from a range of disciplinary perspectives, including philosophy, psychology, economics, health care, and education. The purpose of this introductory chapter is to provide a short primer on the different disciplinary approaches to the study of well-being. To supplement this primer, we also invited key experts from several disciplines-philosophy, psychology, public policy, and health care-to share their thoughts on what they believe are the most important open questions and ethical issues for the multidisciplinary study of digital well-being. We also introduce and discuss several themes that we believe will be fundamental to the ongoing study of digital well-being: digital gratitude, automated interventions, and sustainable co-well-being.

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Principles alone cannot guarantee ethical AI

Cited by 659 publications

References 59 publications

Robo-investment aversion

Robo-investment aversion

Primer on an ethics of AI-based decision support systems in the clinic

The Ethics of Digital Well-Being: A Multidisciplinary Perspective

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