Challenging the Myth of AI Autonomy
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Organización Internacional del Trabajo (OIT)
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| Format: | Artículo científico |
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International Labour Organization
01/05/2026
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| _version_ | 1874524901023416324 |
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| author | Uma Rani Morgan Williams |
| author_facet | Uma Rani Morgan Williams Uma Rani Morgan Williams |
| contents | Challenging the Myth of AI Autonomy Uma Rani Morgan Williams A common misconception is that artificial intelligence (AI) functions magically, without human intervention. In reality, AI operates within a socio-technical system reliant on vast amounts of human labor throughout its lifecycle. AI depends on two forms of human labor: “algorithmic worker” (the workers responsible for coding and fine-tuning models) and “data worker” (those responsible for labeling, cleaning, and expanding datasets to train AI models). Both are crucial, but data worker often remains invisible to the end-user, leaving workers vulnerable to decent work deficits. This piece examines how these two forms of labor are interconnected and discusses the working conditions of data workers in India and Kenya based on recent ILO surveys conducted in 2022–23. Finally, it explores measures for enhancing transparency and accountability in AI development to ensure that this often-undervalued work and these invisible workers receive the recognition they deserve. 10.34669/wi.wjds/6.1.6 DOI https://doi.org/10.34669/wi.wjds/6.1.6 publication.journalArticle |
| format | Artículo científico |
| id | ilo_995703567802676 |
| institution | Organización Internacional del Trabajo (OIT) |
| publishDate | 01/05/2026 |
| publisher | International Labour Organization |
| spellingShingle | Challenging the Myth of AI Autonomy Uma Rani Morgan Williams Challenging the Myth of AI Autonomy Uma Rani Morgan Williams A common misconception is that artificial intelligence (AI) functions magically, without human intervention. In reality, AI operates within a socio-technical system reliant on vast amounts of human labor throughout its lifecycle. AI depends on two forms of human labor: “algorithmic worker” (the workers responsible for coding and fine-tuning models) and “data worker” (those responsible for labeling, cleaning, and expanding datasets to train AI models). Both are crucial, but data worker often remains invisible to the end-user, leaving workers vulnerable to decent work deficits. This piece examines how these two forms of labor are interconnected and discusses the working conditions of data workers in India and Kenya based on recent ILO surveys conducted in 2022–23. Finally, it explores measures for enhancing transparency and accountability in AI development to ensure that this often-undervalued work and these invisible workers receive the recognition they deserve. 10.34669/wi.wjds/6.1.6 DOI https://doi.org/10.34669/wi.wjds/6.1.6 publication.journalArticle |
| title | Challenging the Myth of AI Autonomy |
| url | https://researchrepository.ilo.org/esploro/outputs/journalArticle/Challenging-the-Myth-of-AI-Autonomy/995703567802676 |