(Un)healthy algorithms? Trade union, employer, and government action for safety and health in algorithmically managed work

Abstract Algorithmic management (AM) is reshaping work in many industries. However, what is done to redress potential risks is little understood. This study explores how trade unions, employers, and government actors assess AM-related occupational safety and health (OSH) risks and their strategies to understand how industrial relations could influence the safety and health of workers managed by digital technologies. Drawing on the Pressure, Disorganisation and Regulatory failure (PDR) model and interview and document data from Sweden, we find a gradually increasing interest in AM in the early 2020s among the government and the social partners. Unions learn, inform, and bargain about AM; employers enact ‘healthy discipline’; and government agencies inspect digital risks in workplaces. Moreover, economic and reward pressures contribute to AM-associated OSH risks. Disorganisation manifests as a lack of knowledge about the OSH effects of AM, leading to ineffective OSH management. Regulatory failure is reflected in new EU regulations stalling national-level initiatives, since the overlapping regulations complicate the enforcement of existing OSH regulations. This study highlights the crucial role of trade unions in advancing the agenda on AM-related OSH risks. It also makes a theoretical contribution by extending the PDR model, offering insights into the driving forces shaping AM and compromising OSH beyond the workplace level – highlighting wider politico-economic and institutional dynamics influencing OSH.

Authors

Institutions

Publication Details

Journal
The Economic and Labour Relations Review
Published
2026-01-29
DOI
https://doi.org/10.1017/elr.2025.10046
Primary Topic
Digital Economy and Work Transformation
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

(Un)healthy algorithms? Trade union, employer, and government action for safety and health in algorithmically managed work

Carin Håkansta, Emma Brulin, Kerstin Nilsson, Ruben Lind et al.
The Economic and Labour Relations Review
Digital Economy and Work Transformation
article

(Un)healthy algorithms? Trade union, employer, and government action for safety and health in algorithmically managed work

Carin Håkansta, Emma Brulin, Kerstin Nilsson, Ruben Lind, Pille Strauss, Min Kyung Lee, Michael Quinlan
article en

Abstract

Abstract Algorithmic management (AM) is reshaping work in many industries. However, what is done to redress potential risks is little understood. This study explores how trade unions, employers, and government actors assess AM-related occupational safety and health (OSH) risks and their strategies to understand how industrial relations could influence the safety and health of workers managed by digital technologies. Drawing on the Pressure, Disorganisation and Regulatory failure (PDR) model and interview and document data from Sweden, we find a gradually increasing interest in AM in the early 2020s among the government and the social partners. Unions learn, inform, and bargain about AM; employers enact ‘healthy discipline’; and government agencies inspect digital risks in workplaces. Moreover, economic and reward pressures contribute to AM-associated OSH risks. Disorganisation manifests as a lack of knowledge about the OSH effects of AM, leading to ineffective OSH management. Regulatory failure is reflected in new EU regulations stalling national-level initiatives, since the overlapping regulations complicate the enforcement of existing OSH regulations. This study highlights the crucial role of trade unions in advancing the agenda on AM-related OSH risks. It also makes a theoretical contribution by extending the PDR model, offering insights into the driving forces shaping AM and compromising OSH beyond the workplace level – highlighting wider politico-economic and institutional dynamics influencing OSH.

The Economic and Labour Relations Review
Karolinska Institutet (SE), UNSW Sydney (AU), The University of Texas at Austin (US)
AFA Försäkring
Decent work and economic growth
Openalex Percentile: Top 78%
Digital Economy and Work Transformation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.