AI at work, algorithmic bosses, and the ambivalence of automation
Bibliographic record
Abstract
This chapter revisits a foundational question (“what do bosses do?”) to explore how Artificial Intelligence (AI) is reshaping power dynamics in the workplace. Far from neutral tools of optimisation, algorithmic systems increasingly amplify managerial prerogatives, embedding them into automated processes that are difficult to scrutinise, govern or contest. These developments give rise to a paradox: managers and workers are simultaneously augmented and disempowered, caught in systems that intensify control while eroding autonomy. Drawing on legal, organisational and regulatory perspectives, we argue that existing safeguards, ranging from data protection rights to information and consultation, are underenforced and ill-equipped to confront this shift. The chapter critiques the illusion of perfunctory compliance and calls for a structural rethinking of workplace technology governance. We also emphasize a missed opportunity: rather than reinforcing top-down hierarchies, AI systems could be leveraged to democratise the workplace, enhance workers’ agency and enable new models of participation. Doing so, however, requires confronting the socio-legal assumptions that sustain current forms of digital control—and imagining alternative futures where technology serves workers, not just those who manage them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".