为什么我们应该谈论机构 (不) 可信性和医疗机器学习
Michiel De Proost1, Giorgia Pozzi2
1Bioethics Institute Ghent, Department of Philosophy and Moral Sciences, Ghent University, Blandijnberg 2, Ghent, 9000, Belgium. michiel.deproost@ugent.be.
Medicine, health care, and philosophy
|November 13, 2024
概括
了解机构的不信任度是通过医学机器学习来导航道德参与的关键. 本分析探讨了如何通过检查机构不透明度及其对医疗保健AI信任的影响来避免不信任.
科学领域:
- 医疗人工智能 伦理 医学 人工智能
- 临床机器学习信任信任
- 技术的哲学技术的哲学
背景情况:
- 信任是参与临床机器学习 (ML) 系统的核心.
- 在ML中,信任和不信任的概念是正在进行的哲学和伦理辩论的主题.
- 在医学ML应用的背景下,一种不信任的气氛正在出现.
研究的目的:
- 分析医学ML中的"机构不信任"的概念.
- 提供如何不与医疗ML系统接触的诊断.
- 确定修复机构不信任性的构建块.
主要方法:
- 结构分析"ex negativo"侧重于不值得信赖的情况.
- 审查机构不透明度及其在破坏信任方面的作用.
- 扩大霍利的承诺账户,以定义机构的可信度.
主要成果:
- 机构的不透明性可能会侵蚀医疗机构的可信度.
- 医学ML中的不透明性可能导致新型的证词不公正.
- 了解如何不参与对于建立对医疗保健的信任至关重要.
结论:
- 解决机构的不信任性对于道德医学ML部署至关重要.
- 需要策略来打击不透明和证词不公正.
- 修复不信任需要专注于建立强大的机构可信度.
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