医疗保健领域的人工智能--信息系统的可解释性伦理要求:对原因的系统性审查。

IF 3 1区 哲学 Q1 ETHICS
Nils Freyer, Dominik Groß, Myriam Lipprandt
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引用次数: 0

摘要

背景:尽管人工智能决策支持系统(AI-DSS)的性能不断提高,尤其是在临床环境中,但其面临的一个主要挑战仍然是认识上的不透明性。在医疗保健领域合理使用偶尔无法解释的技术的条件和解决方案是一个活跃的研究领域。2024 年 3 月,欧盟就《人工智能法案》(AIA)达成一致,要求医疗人工智能-DSS 必须是可临时解释的,或使用可事后解释的方法。关于这一要求的伦理辩论似乎尚未尘埃落定。本系统综述旨在概述伦理辩论中的立场和论点,并对其进行分类:我们在 PubMed、BASE 和 Scopus 上对 2016 年至 2024 年的英语科学同行评审出版物进行了文献检索。纳入标准是对医疗保健中的人工智能--信息系统的可解释性提出明确要求,并说明理由。非特定领域的文件以及调查、综述和荟萃分析均被排除在外。根据可解释性要求的论据和所需的可解释性水平,对文献中概述的可解释性伦理要求进行了定性分析:文献检索结果为 1662 篇文献;在对其余全文进行资格筛选后,44 篇文献被纳入综述。我们的分析表明,17 篇文献支持可解释人工智能方法(xAI)或临时可解释模型的要求,提供了 9 类论据。其他 27 条记录反对一般要求,提供了 11 类论据。此外,我们还发现有 14 篇作品主张需要根据上下文确定可解释性的水平,而有 30 篇文献则主张与上下文无关的绝对标准:对理由的系统性回顾表明,在医疗保健领域的人工智能数据系统是否需要事后可解释性方法或临时可解释性模型的问题上,并没有达成明确的一致意见。辩论中发现的论点从不同的角度进行了引用和回应,显示了一种互动式的讨论。政策制定者和研究人员应密切关注辩论的发展。反之,鉴于该领域的频繁进步,伦理学家应充分了解经验和技术研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The ethical requirement of explainability for AI-DSS in healthcare: a systematic review of reasons.

Background: Despite continuous performance improvements, especially in clinical contexts, a major challenge of Artificial Intelligence based Decision Support Systems (AI-DSS) remains their degree of epistemic opacity. The conditions of and the solutions for the justified use of the occasionally unexplainable technology in healthcare are an active field of research. In March 2024, the European Union agreed upon the Artificial Intelligence Act (AIA), requiring medical AI-DSS to be ad-hoc explainable or to use post-hoc explainability methods. The ethical debate does not seem to settle on this requirement yet. This systematic review aims to outline and categorize the positions and arguments in the ethical debate.

Methods: We conducted a literature search on PubMed, BASE, and Scopus for English-speaking scientific peer-reviewed publications from 2016 to 2024. The inclusion criterion was to give explicit requirements of explainability for AI-DSS in healthcare and reason for it. Non-domain-specific documents, as well as surveys, reviews, and meta-analyses were excluded. The ethical requirements for explainability outlined in the documents were qualitatively analyzed with respect to arguments for the requirement of explainability and the required level of explainability.

Results: The literature search resulted in 1662 documents; 44 documents were included in the review after eligibility screening of the remaining full texts. Our analysis showed that 17 records argue in favor of the requirement of explainable AI methods (xAI) or ad-hoc explainable models, providing 9 categories of arguments. The other 27 records argued against a general requirement, providing 11 categories of arguments. Also, we found that 14 works advocate the need for context-dependent levels of explainability, as opposed to 30 documents, arguing for context-independent, absolute standards.

Conclusions: The systematic review of reasons shows no clear agreement on the requirement of post-hoc explainability methods or ad-hoc explainable models for AI-DSS in healthcare. The arguments found in the debate were referenced and responded to from different perspectives, demonstrating an interactive discourse. Policymakers and researchers should watch the development of the debate closely. Conversely, ethicists should be well informed by empirical and technical research, given the frequency of advancements in the field.

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来源期刊
BMC Medical Ethics
BMC Medical Ethics MEDICAL ETHICS-
CiteScore
5.20
自引率
7.40%
发文量
108
审稿时长
>12 weeks
期刊介绍: BMC Medical Ethics is an open access journal publishing original peer-reviewed research articles in relation to the ethical aspects of biomedical research and clinical practice, including professional choices and conduct, medical technologies, healthcare systems and health policies.
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