Sociotechnical Challenges of Machine Learning in Healthcare and Social Welfare

2026-01-16

AI Summary

This study addresses the frequent failure of machine learning tools in care-oriented domains such as healthcare and social welfare, where they often become misaligned with real-world practices. To bridge this gap, the authors develop a sociotechnical analytical framework grounded in qualitative fieldwork, longitudinal case studies, and co-design workshops with frontline practitioners. The framework introduces a taxonomy of 11 distinct challenges and a process model that explains how these challenges emerge over time within actual care pathways. By articulating the sociotechnical tensions and their underlying mechanisms in accessible yet precise terms, the work offers both theoretical grounding and practical guidance for diagnosing problems and designing more contextually appropriate machine learning systems across diverse care settings.

Technology Category

• Machine Learning: Causal Learning
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Application Domains: Humanities & Computational Social Science

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Socio-technical web-based systems

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.

Problem

• sociotechnical challenges
• machine learning
• healthcare
• social welfare
• care practices

Innovation

• sociotechnical challenges
• machine learning in healthcare
• care pathway
• co-design
• qualitative fieldwork