Between risk mitigation and labour rights enforcement: Assessing the transatlantic race to govern AI-driven decision-making through a comparative lens
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this article, we provide an overview of efforts to regulate the various phases of the artificial intelligence (AI) life cycle. In doing so, we examine whether—and, if so, to what extent—highly fragmented legal frameworks are able to provide safeguards capable of preventing the dangers that stem from AI- and algorithm-driven organisational practices. We critically analyse related developments at the European Union (EU) level, namely the General Data Protection Regulation, the draft AI Regulation, and the proposal for a Directive on improving working conditions in platform work. We also consider bills and regulations proposed or adopted in the United States and Canada via a transatlantic comparative approach, underlining analogies and variations between EU and North American attitudes towards the risk assessment and management of AI systems. We aim to answer the following questions: Is the widely adopted risk-based approach fit for purpose? Is it consistent with the actual enforcement of fundamental rights at work, such as privacy, human dignity, equality and collective rights? To answer these questions, in section 2 we unpack the various, often ambiguous, facets of the notion(s) of ‘risk’—that is, the common denominator with the EU and North American legal instruments. Here, we determine that a scalable, decentralised framework is not appropriate for ensuring the enforcement of constitutional labour-related rights. In addition to presenting the key provisions of existing schemes in the EU and North America, in section 3 we disentangle the consistencies and tensions between the frameworks that regulate AI and constrain how it must be handled in specific contexts, such as work environments and platform-orchestrated arrangements. Paradoxically, the frenzied race to regulate AI-driven decision-making could exacerbate the current legal uncertainty and pave the way for regulatory arbitrage. Such a scenario would slow technological innovation and egregiously undermine labour rights. Thus, in section 4 we advocate for the adoption of a dedicated legal instrument at the supranational level to govern technologies that manage people in workplaces. Given the high stakes involved, we conclude by stressing the salience of a multi-stakeholder AI governance framework.
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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 | 0.002 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it