Heinrichs Jan-Hendrik
Institute for Neuroscience and Medicine 7: Brain and Behaviour, Forschungszentrum Jülich GmbH, Jülich, Germany; Institute of Philosophy, RWTH Aachen University, Aachen, Germany.
Camb Q Healthc Ethics. 2023 May 16:1-12. doi: 10.1017/S0963180123000257.
Artificial intelligence (AI) is a powerful tool for several healthcare tasks. AI tools are suited to optimize predictive models in medicine. Ethical debates about AI's extension of the predictive power of medical models suggest a need to adapt core principles of medical ethics. This article demonstrates that a popular interpretation of the principle of justice in healthcare needs amendment given the effect of AI on decision-making. The procedural approach to justice, exemplified with Norman Daniels and James Sabin's conception, needs amendment because, as research into algorithmic fairness shows, it is insufficiently sensitive to differential effects of seemingly just principles on different groups of people. The same line of research generates methods to quantify differential effects and make them amenable for correction. Thus, what is needed to improve the principle of justice is a combination of procedures for selecting just criteria and principles and the use of algorithmic tools to measure the real impact these criteria and principles have. In this article, the author shows that algorithmic tools do not merely raise issues of justice but can also be used in their mitigation by informing us about the real effects certain distributional principles and criteria would create.
人工智能（AI）是用于多项医疗保健任务的强大工具。人工智能工具适合优化医学预测模型。关于人工智能扩展医学模型预测能力的伦理辩论表明，需要调整医学伦理的核心原则。本文表明，鉴于人工智能对决策的影响，对医疗保健中正义原则的一种流行解释需要修正。以诺曼·丹尼尔斯（Norman Daniels）和詹姆斯·萨宾（James Sabin）的概念为例的程序正义方法需要修正，因为正如对算法公平性的研究所表明的那样，它对看似公正的原则对不同人群的不同影响不够敏感。同一研究方向产生了量化不同影响并使其便于纠正的方法。因此，改进正义原则需要将选择公正标准和原则的程序与使用算法工具来衡量这些标准和原则的实际影响相结合。在本文中，作者表明算法工具不仅引发了正义问题，还可以通过告知我们某些分配原则和标准将产生的实际影响来用于缓解这些问题。
