Safeguarding Autonomy: a Focus on Machine Learning Decision Systems
It tackles the problem of integrating ethical autonomy principles into real-world ML systems for practitioners and regulators, though it appears incremental as it builds on existing theoretical frameworks.
This paper addresses the gap between theoretical discussions of autonomy in machine learning and practical implementation by identifying factors that prevent its application in ML decision systems, proposing questions to guide practitioners in respecting end-user autonomy throughout the ML pipeline.
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.