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Trustworthy and human-centric? The new governance of workplace AI technologies under the EU’s Artificial Intelligence Act

Didem Özkızıltan & Fabio Landini

Transfer: European review of labour and research2025https://doi.org/10.1177/10242589251336193article
AJG 2ABDC C
Weight
0.41

What the paper says

The increasing use of AI technologies in the workplace raises significant concerns about their implications for workers’ rights and the future of work. The EU’s Artificial Intelligence Act aims to ensure that innovation is not at the expense of fundamental rights. While the Act seeks to promote human-centric and trustworthy AI applications, this article highlights several regulatory gaps that are likely to foster two key trends shaping the future of work in Europe: the increasing autonomy of AI tech companies over the design of workplace AI and the reinforcement of power imbalances in the workplace. These trends leave EU workplaces vulnerable to biased and intrusive AI, thereby hindering the fair integration of AI into the work of the future. This article critiques the Act’s shortcomings in regulating AI at work and advocates a dedicated EU directive to mitigate adverse impacts of AI in the workplace and reinforce human-centric AI principles for societal benefit.

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https://doi.org/https://doi.org/10.1177/10242589251336193

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@article{didem2025,
  title        = {{Trustworthy and human-centric? The new governance of workplace AI technologies under the EU’s Artificial Intelligence Act}},
  author       = {Didem Özkızıltan & Fabio Landini},
  journal      = {Transfer: European review of labour and research},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/10242589251336193},
}

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Trustworthy and human-centric? The new governance of workplace AI technologies under the EU’s Artificial Intelligence Act

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.