Sociotechnical challenges of machine learning in healthcare and social welfare are mismatches between how a machine learning tool functions and the structure of care practices. While prior research has documented many such issues, existing accounts often attribute them either to designers' limited social understanding or to inherent technical constraints, offering limited support for systematic description and comparison across settings. In this paper, we present a framework for conceptualizing sociotechnical challenges of machine learning grounded in qualitative fieldwork, a review of longitudinal deployment studies, and co-design workshops with healthcare and social welfare practitioners. The framework comprises (1) a categorization of eleven sociotechnical challenges organized along an ML-enabled care pathway, and (2) a process-oriented account of the conditions through which these challenges emerge across design and use. By providing a parsimonious vocabulary and an explanatory lens focused on practice, this work supports more precise analysis of how machine learning tools function and malfunction within real-world care delivery.

翻译：医疗保健与社会福利中机器学习的社会技术挑战，是指机器学习工具的运行方式与照护实践结构之间的不匹配。尽管已有研究记录了许多此类问题，但现有论述通常将其归因于设计者有限的社会认知或固有的技术限制，难以为跨情境的系统性描述与比较提供有效支持。本文提出一个概念化机器学习社会技术挑战的框架，该框架基于质性实地调研、纵向部署研究综述，以及与医疗保健和社会福利从业者共同开展的合作设计研讨会。该框架包含：（1）沿循机器学习赋能的照护路径归纳的十一类社会技术挑战；（2）对这些挑战在设计与使用过程中如何产生的条件进行过程化阐释。通过提供一套精炼的术语体系及聚焦实践的解释视角，本研究有助于更精确地分析机器学习工具在现实世界照护服务中的有效运行与故障机制。