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Modern advances in AI have increased employer interest in tracking workers’ biometric signals — e.g., their brainwaves and facial expressions — to evaluate and make predictions about their performance and productivity. These technologies afford managers information about internal emotional and physiological states that were previously accessible only to individual workers, raising new concerns around worker privacy and autonomy. Yet, the research literature on the impact of AI-powered biometric work monitoring (AI-BWM) technologies on workers remains fragmented across disciplines and industry sectors, limiting our understanding of its impacts on workers at large. In this paper, we sytematically review 129 papers, spanning varied disciplines and industry sectors, that discuss and analyze the impact of AI-powered biometric monitoring technologies in occupational settings. We situate this literature across a process model that spans the development, deployment, and usage phases of these technologies. We further draw on Shelby et al.’s Taxonomy of Socio-technical Harms in AI systems to systematize the harms experienced by workers across the three phases of our process model. We find that the development, deployment, and sustained use of AI-powered biometric work monitoring technologies put workers at risk of a number of the socio-technical harms specified by Shelby et al.: e.g., by forcing workers to exert additional emotional labor to avoid flagging unreliable affect monitoring systems, or through the use of these data to make inferences about productivity. Our research contributes to the field of critical AI studies by highlighting the potential for a cascade of harms to occur when the impact of these technologies on workers is not considered at all phases of our process model.
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Algorithms at Work: The New Contested Terrain of Control
The widespread implementation of algorithmic technologies in organizations prompts questions about how algorithms may reshape organizational control. We use Edwards’ (1979) perspective of “contested terrain,” wherein managers implement production technologies to maximize the value of labor and workers resist, to synthesize the interdisciplinary research on algorithms at work. We find that algorithmic control in the workplace operates through six main mechanisms, which we call the “6 Rs”—employers can use algorithms to direct workers by restricting and recommending, evaluate workers by recording and rating, and discipline workers by replacing and rewarding. We also discuss several key insights regarding algorithmic control. First, labor process theory helps to highlight potential problems with the largely positive view of algorithms at work. Second, the technical capabilities of algorithmic systems facilitate a form of rational control that is distinct from the technical and bureaucratic control used by employers for the past century. Third, employers’ use of algorithms is sparking the development of new algorithmic occupations. Finally, workers are individually and collectively resisting algorithmic control through a set of emerging tactics we call algoactivism. These insights sketch the contested terrain of algorithmic control and map critical areas for future research.
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- Award ID(s):
- 1847091
- PAR ID:
- 10195395
- Date Published:
- Journal Name:
- The Academy of Management annals
- Volume:
- 14
- Issue:
- 1
- ISSN:
- 1941-6067
- Page Range / eLocation ID:
- 366–410
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5465/annals.2018.0174
