Defending explicability as a principle for the ethics of artificial intelligence in medicine.

Artificial intelligence Explicability Medical ethics Principlism

Journal

Medicine, health care, and philosophy

ISSN: 1572-8633

Titre abrégé: Med Health Care Philos

Pays: Netherlands

ID NLM: 9815900

Informations de publication

Date de publication:
29 Aug 2023

Historique:

accepted: 16 08 2023

medline: 29 8 2023

pubmed: 29 8 2023

entrez: 29 8 2023

Statut: aheadofprint

Résumé

The difficulty of explaining the outputs of artificial intelligence (AI) models and what has led to them is a notorious ethical problem wherever these technologies are applied, including in the medical domain, and one that has no obvious solution. This paper examines the proposal, made by Luciano Floridi and colleagues, to include a new 'principle of explicability' alongside the traditional four principles of bioethics that make up the theory of 'principlism'. It specifically responds to a recent set of criticisms that challenge the supposed need for such a principle to perform an enabling role in relation to the traditional four principles and therefore suggest that these four are sufficient without the addition of explicability. The paper challenges the critics' premise that explicability cannot be an ethical principle like the classic four because it is explicitly subordinate to them. It argues instead that principlism in its original formulation locates the justification for ethical principles in a midlevel position such that they mediate between the most general moral norms and the contextual requirements of medicine. This conception of an ethical principle then provides a mold for an approach to explicability on which it functions as an enabling principle that unifies technical/epistemic demands on AI and the requirements of high-level ethical theories. The paper finishes by anticipating an objection that decision-making by clinicians and AI fall equally, but implausibly, under the principle of explicability's scope, which it rejects on the grounds that human decisions, unlike AI's, can be explained by their social environments.

Identifiants

DOI: 10.1007/s11019-023-10175-7 PMID: 37642834

pubmed: 37642834

doi: 10.1007/s11019-023-10175-7

pii: 10.1007/s11019-023-10175-7

doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

Informations de copyright

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