探讨机器学习如何影响用户自主性并提出实践建议
Safeguarding Autonomy: a Focus on Machine Learning Decision Systems
- 从机器学习全链路分析影响用户自主性的关键因素
- 识别多个阶段对用户决策权的潜在削弱作用
- 为开发者提供可操作问题清单以尊重用户自主
随着全球对人工智能监管的讨论日益升温，本文聚焦机器学习对自主性的影响并提升相关认知。自主性是生物伦理学中的基本原则，强调个体应作为自身决策者。尽管该概念在多个欧洲规范性文件中被提及，但在机器学习实践中尚未广泛采纳。本文旨在弥合理论与实践之间的鸿沟，通过识别当前阻碍自主性应用的关键条件，推动其在机器学习决策中的实际落地。我们系统分析了机器学习全生命周期中可能影响最终用户自主性的环节，并针对每个发现的影响提出相应问题，为识别可改进的着力点提供指导，增强决策过程中对用户自主权的尊重。
原文摘要 · Abstract (English)
As global discourse on AI regulation gains momentum, this paper focuses on delineating the impact of ML on autonomy and fostering awareness. Respect for autonomy is a basic principle in bioethics that establishes persons as decision-makers. While the concept of autonomy in the context of ML appears in several European normative publications, it remains a theoretical concept that has yet to be widely accepted in ML practice. Our contribution is to bridge the theoretical and practical gap by encouraging the practical application of autonomy in decision-making within ML practice by identifying the conditioning factors that currently prevent it. Consequently, we focus on the different stages of the ML pipeline to identify the potential effects on ML end-users' autonomy. To improve its practical utility, we propose a related question for each detected impact, offering guidance for identifying possible focus points to respect ML end-users autonomy in decision-making.
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