Sociotechnical Challenges of Machine Learning in Healthcare and Social Welfare

TL;DR

Using qualitative fieldwork and a synthesis of longitudinal deployment studies plus co-design workshops, the paper develops a pragmatic framework of 11 sociotechnical challenges along the ML-enabled care pathway and a three-process model for their emergence. It locates these challenges within Technologies-in-Practice and the duality of structure, arguing that use-phase dynamics, not just design, shape failure and adaptation. The contributions include a parsimonious vocabulary and analytic lens to describe how ML-enabled care functions and falters in real settings, beyond training data or ethical risk discussions. The framework aims to improve deployment, governance, and alignment of ML-supported care with professional norms and workflows.

Abstract

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.