The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Prisoners Training AI
Open accessAbstract
Despite accounts of how artificial intelligence (AI) is replacing human labour, constant efforts are needed to keep AI-based automation running. In this chapter, we are particularly interested in data work that supports processes of automation. We explore an unconventional arrangement in which Finnish prisoners annotate text to produce training data for a local AI firm. The use of prison labour to train AI invites straightforward conclusions of exploitation of the marginalised. On a closer look, however, attention is drawn to local and situational variations of data labour: how high-tech development can be married with humane penal policies and low-cost labour with rehabilitative aspirations. We argue for an approach that can hold together seemingly contradictory value aims and open novel ways of exploring processes of automated decision-making. By acknowledging what is of value to the different parties involved, we can begin to see alternative paths forward in the study of automation.
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“Burnout is an occupational phenomenon, not a medical condition, and should not be equated with depression [20,109].”
From Measuring the Mental Health of Content Reviewers, a Systematic Review (2025) · cites this paper as [109]
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“Since occupational psychologists first developed burnout to study professionals in health and human services in 1981, it has become the most-researched measurement of a person's negative experience in their workplace [109,158].”
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“For instance, the World Health Organization has stated that burnout is an occupational phenomenon, not a medical condition [109,191].”
From Measuring the Mental Health of Content Reviewers, a Systematic Review (2025) · cites this paper as [109]
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“Instead, the WHO positions burnout as a condition related to the context in which work occurs rather than an individuals' personal weaknesses or diagnosis [109,191].”
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Citations
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AI in Context: The Labor of Integrating New Technologies
Mateescu, A., & Elish, M. C. (2019) · doi:10.69985/xtet4824 · Cited by 49
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Cited by
Sorted by citations
‘We can see a savage’: a case study of the colonial gaze in generative AI algorithms
Alenichev, A., Shaffer, J. D., Kingori, P., Grietens, K. P., Muldoon, J., & Rocher, L. (2026) · AI & SOCIETY · doi:10.1007/s00146-025-02685-0 · Cited by 3
Measuring the Mental Health of Content Reviewers, a Systematic Review
Gonzalez, A., & Matias, J. N. (2025) · arXiv · doi:10.48550/arxiv.2502.00244 · Cited by 1
Metadata
- Authors
- Lehtiniemi, T., & Ruckenstein, M.
- Venue
- Everyday Automation
- Publisher
- Routledge
- Published
- 2022-05-09
- Pages
- 184-196
- OpenAlex
- W4221103008
- Cited by
- 11
- References
- 3
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 3 · 2023: 4
- FWCI
- 8.92
- Type
- book chapter
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Routledge eBooks (publishedVersion, cc-by-nc-nd)
- Työväentutkimus Vuosikirja (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Industrial relations › Legal and Labor Studies
- Social Sciences › Sociology and Political Science › Criminal Justice and Corrections Analysis
- Keywords
- prison labor, data annotation, training data, AI training, automation, exploitation, rehabilitation, criminal policy, automated decision-making, prisoners, cheap labor
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