Beyond bias and discrimination: redefining the AI ethics principle of fairness in healthcare machine-learning algorithms.
Beyond bias and discrimination: redefining the AI ethics principle of fairness in healthcare machine-learning algorithms.
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DOI：
10.1007/s00146-022-01455-6
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发表时间：
2023
期刊：
影响因子：
3
通讯作者：
Tiribelli, Simona
中科院分区：
文献类型：
--
作者：
Giovanola, Benedetta;Tiribelli, Simona
随着越来越多地实施和依赖机器学习（ML）算法来执行任务，提供服务并在健康和医疗保健中做出决策，这使得对ML公平性的需求，更具体地说，医疗保健ML算法（HMLA）是一项非常重要和紧迫的任务。然而，尽管在过去十年中，人工智能（AI）和HMLA伦理中关于公平的争论显着增加，但公平作为一种道德价值观的概念尚未得到充分探讨。我们的论文旨在填补这一空白，并从概念的角度来解决公平的AI伦理原则，从道德哲学中阐述的公平账户中汲取见解，并利用它们将公平概念化为一种道德价值，并相应地重新定义HMLA中的公平。为了实现我们的目标，在第一部分旨在澄清本文的背景，方法和结构之后，在第二部分中，我们概述了HMLA中公平的AI伦理原则的讨论，并表明这场辩论背后的公平概念是纯粹分配性的，并与非歧视重叠，这反过来又被定义为没有偏见。在表明这种框架是不充分的，在第三部分，我们追求一个伦理调查的公平概念，并认为公平应该被视为一种伦理价值。在澄清公平与不歧视之间的关系之后，我们表明，两者并不重叠，公平要求的不仅仅是不歧视。此外，我们强调，公平不仅有一个分配，但也是一个社会关系的层面。最后，我们指出公平的构成要素。在这样做时，我们的论点是基于对尊重概念的重新思考，这一概念超越了平等尊重的概念，包括对个人的尊重。在第四部分中，我们分析了我们的概念重新定义的公平作为一种道德价值的公平在HMLA的讨论的影响。在这里，我们主张，公平不仅仅要求不歧视和没有偏见，也不仅仅要求公正的分配;它需要确保HMLA尊重人作为人和特定的个人。最后，在第五部分中，我们概述了一些更广泛的影响，并展示了我们的调查如何有助于使HMLA和更普遍的AI促进社会公益和更公平的社会。
The increasing implementation of and reliance on machine-learning (ML) algorithms to perform tasks, deliver services and make decisions in health and healthcare have made the need for fairness in ML, and more specifically in healthcare ML algorithms (HMLA), a very important and urgent task. However, while the debate on fairness in the ethics of artificial intelligence (AI) and in HMLA has grown significantly over the last decade, the very concept of fairness as an ethical value has not yet been sufficiently explored. Our paper aims to fill this gap and address the AI ethics principle of fairness from a conceptual standpoint, drawing insights from accounts of fairness elaborated in moral philosophy and using them to conceptualise fairness as an ethical value and to redefine fairness in HMLA accordingly. To achieve our goal, following a first section aimed at clarifying the background, methodology and structure of the paper, in the second section, we provide an overview of the discussion of the AI ethics principle of fairness in HMLA and show that the concept of fairness underlying this debate is framed in purely distributive terms and overlaps with non-discrimination, which is defined in turn as the absence of biases. After showing that this framing is inadequate, in the third section, we pursue an ethical inquiry into the concept of fairness and argue that fairness ought to be conceived of as an ethical value. Following a clarification of the relationship between fairness and non-discrimination, we show that the two do not overlap and that fairness requires much more than just non-discrimination. Moreover, we highlight that fairness not only has a distributive but also a socio-relational dimension. Finally, we pinpoint the constitutive components of fairness. In doing so, we base our arguments on a renewed reflection on the concept of respect, which goes beyond the idea of equal respect to include respect for individual persons. In the fourth section, we analyse the implications of our conceptual redefinition of fairness as an ethical value in the discussion of fairness in HMLA. Here, we claim that fairness requires more than non-discrimination and the absence of biases as well as more than just distribution; it needs to ensure that HMLA respects persons both as persons and as particular individuals. Finally, in the fifth section, we sketch some broader implications and show how our inquiry can contribute to making HMLA and, more generally, AI promote the social good and a fairer society.
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