Should explainability be a fifth ethical principle in AI ethics?

Cortese, João; Cozman, Fábio Gagliardi; Lucca-Silveira, Marcos Paulo de; Bechara, Adriano Figueiredo

doi:10.1007/s43681-022-00152-w

Cited by 7 publications

(2 citation statements)

References 27 publications

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“…Although the same term is not used in computer science (see appendix), it has been introduced to the debate on ethical AI by Floridi et al (2018). Notions of explicability can be found in high-level guidelines on ethical AI (AI4People in Floridi et al 2018;Robbins 2019, p. 499), although its conceptual value has been contested (Ursin et al 2022;Cortese et al 2022;Wadden 2021;Krishnan 2020;Mittelstadt 2019;Robbins 2019).…”

Section: Mapping Concepts Of Explicabilitymentioning

confidence: 99%

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Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?

et al. 2023

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Definition of the problem The umbrella term “explicability” refers to the reduction of opacity of artificial intelligence (AI) systems. These efforts are challenging for medical AI applications because higher accuracy often comes at the cost of increased opacity. This entails ethical tensions because physicians and patients desire to trace how results are produced without compromising the performance of AI systems. The centrality of explicability within the informed consent process for medical AI systems compels an ethical reflection on the trade-offs. Which levels of explicability are needed to obtain informed consent when utilizing medical AI? Arguments We proceed in five steps: First, we map the terms commonly associated with explicability as described in the ethics and computer science literature, i.e., disclosure, intelligibility, interpretability, and explainability. Second, we conduct a conceptual analysis of the ethical requirements for explicability when it comes to informed consent. Third, we distinguish hurdles for explicability in terms of epistemic and explanatory opacity. Fourth, this then allows to conclude the level of explicability physicians must reach and what patients can expect. In a final step, we show how the identified levels of explicability can technically be met from the perspective of computer science. Throughout our work, we take diagnostic AI systems in radiology as an example. Conclusion We determined four levels of explicability that need to be distinguished for ethically defensible informed consent processes and showed how developers of medical AI can technically meet these requirements.

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Section: Mapping Concepts Of Explicabilitymentioning

confidence: 99%

“…Explicability is not considered a moral principle on its own, comparable to the other four principles of biomedical ethics (Cortese et al 2022;Morley et al 2020). It is linked to them by mostly instrumental chains to avoid harm and increase trust or performance (Ursin et al 2022;McCoy et al 2022).…”

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confidence: 99%

Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?

et al. 2023

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Take five? A coherentist argument why medical AI does not require a new ethical principle

Segers,

De Proost

2024

Theor Med Bioeth

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

Adams

2023

Med Health Care and Philos

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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.

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Should explainability be a fifth ethical principle in AI ethics?

Cited by 7 publications

References 27 publications

Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?

Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?

Take five? A coherentist argument why medical AI does not require a new ethical principle

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

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