Directors’ Duties and the Collective Governance of Algorithmic Management Systems
Bibliographic record
Abstract
While algorithmic management has improved corporate performance, it poses potential harm to workers and may jeopardize the long-term sustainability of companies, warranting regulatory intervention. Ex-ante human rights impact assessment of algorithmic management systems (AMS) is critical and has been widely adopted. The impact of AMS on multiple stakeholders, the shared ownership of workplace data, and the need to enhance AMS assessments’ quality and legitimacy may justify the adoption of a collective or multi-stakeholder governance of algorithm assessment. However, many jurisdictions have not embraced this approach. In countries with a shareholder primacy tradition, governance structures for ex-ante AMS assessments often exclude workers from having a voice in the assessment process. Even in jurisdictions adhering to stakeholder-oriented corporate governance models where worker participation in AMS governance is permitted, corporate resistance can significantly hinder such involvement. This paper argues that consideration should be given to expanding directors’ duties, requiring them to collaborate with the AMS assessment process and its collective governance, including facilitating workers’ involvement. Directors’ collaborative duties may help remove significant barriers by obligating them to disclose, coordinate, negotiate, and rectify workplace algorithms to serve the interests of companies and multiple stakeholders, including safeguarding workers’ human rights. The effectiveness of this multi-stakeholder governance of AMS assessments requires directors’ collaborative duties, which can help build efficient, equitable, and sustainable AMS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".