Evidence map›Paper›PMID 42857241›Full record

ArticleSafety and health at work2026

Redefining Worker Protection in the Algorithmic Era: From Algorithmic Justice to Insurance Systems for the Gig Economy.

Jian Lee, Jinha Yoon

Abstract read
In one paragraph

Article in Safety and health at work, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jian LeeDepartment of Public Health, Graduate School Yonsei University, Seoul, Republic of Korea.
Jinha YoonDepartment of Preventive Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid expansion of the digital platform economy has introduced novel occupational hazards driven by algorithmic management. This short communication examines the occupational health challenges faced by gig workers in the context of algorithmic control, focusing on four key mechanisms: trade-offs between economic success and worker health, uncertainty in terms of effort and reward, unreliable assessment of job performance, and incorrect attribution of responsibility. These algorithmic hazards differ substantially from conventional occupational risks and cannot be adequately addressed by existing occupational health and safety frameworks. To address these inequities, we propose redefining occupational health and safety standards under an algorithmic justice framework. Furthermore, we suggest implementing an algorithm tax and an algorithm-dependent insurance system to establish sustainable social safety nets. These measures, along with mandatory disclosure of algorithmic management systems as 'High-Impact AI,' offer a practical regulatory pathway to enforce worker protection and fairness within the evolving legal landscape in the algorithmic era.

Indexed as

Algorithmic justiceAlgorithmic managementAlgorithm taxGig economySocial protection

Identifiers

PMID42857241
PMCPMC13650308

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.