Abstract
The increasing use of artificial intelligence -driven algorithms in workplace surveillance has reshaped managerial control, particularly in platform -based and gig work. These algorithmic surveillance systems automatically and continuously track and evaluate workers, using diverse forms of worker -generated data, often under the assumption of objectivity and fairness. However, algorithmic surveillance embeds and amplifies bias, engendering negative outco mes that affect platform workers and reinforce structural inequalities. Despite these issues, surveillance research overlooks bias as a primary concern, nor does it explore how platform workers experience and respond to injustices arising from biased algor ithmic surveillance systems. This research -in-progress paper therefore develops an integrative conceptual framework that combines workplace surveillance, bias, and resistance. This conceptual framework provides a foundation for future research to map the p athways through which algorithmic surveillance produces biased outcomes, while also recognising workers as active agents capable of contesting and resisting algorithmic surveillance
Recommended Citation
Kayas, Oliver G. and Zamani, Efpraxia, "Resisting Bias in Algorithmic Surveillance: An Integrative Conceptual Framework" (2025). MCIS 2025 Proceedings. 3.
https://aisel.aisnet.org/mcis2025/3