An institutionalist approach to AI ethics: justifying the priority of government regulation over self-regulation

Ferretti, Thomas ORCID: 0000-0003-4683-883X (2022) An institutionalist approach to AI ethics: justifying the priority of government regulation over self-regulation. Moral Philosophy and Politics, 9 (2). 239 - 265. ISSN 2194-5616

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Abstract

This article explores the cooperation of government and the private sector to tackle the ethical dimension of artificial intelligence (AI). The argument draws on the institutionalist approach in philosophy and business ethics defending a 'division of moral labor' between governments and the private sector (Rawls 2001; Scheffler and Munoz-Dardé 2005). The goal and main contribution of this article is to explain how this approach can provide ethical guidelines to the AI industry and to highlight the limits of self-regulation. In what follows, I discuss three institutionalist claims. First, principles of AI ethics should be validated through legitimate democratic processes. Second, compliance with these principles should be secured in a stable way. Third, their implementation in practice should be as efficient as possible. If we accept these claims, there are good reasons to conclude that, in many cases, governments implementing hard regulation are in principle (if not yet in practice) the best instruments to secure an ethical development of AI systems. Where adequate regulation exists, firms should respect the law. But when regulation does not yet exist, helping governments build adequate regulation should be businesses' ethical priority, not self-regulation.

Item Type:	Article
Official URL:	https://www.degruyter.com/journal/key/mopp/html
Additional Information:	© 2021 The Author
Divisions:	Philosophy, Logic and Scientific Method
Subjects:	Q Science > Q Science (General) J Political Science > JC Political theory B Philosophy. Psychology. Religion > BJ Ethics B Philosophy. Psychology. Religion > B Philosophy (General)
Date Deposited:	26 Aug 2021 11:03
Last Modified:	12 Dec 2024 02:37
URI:	http://eprints.lse.ac.uk/id/eprint/111815

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