Governing AI Decision‐Making: Balancing Innovation and Accountability
Bibliographic record
Abstract
This article explores the growing use of algorithmic models to make or inform decisions within the public sector. Amidst a climate of accelerating investment, expanding system applicability, and rapid technical progress, it concentrates on how key jurisdictions, most prominently the “digital empires” of the United States, European Union, and China, construct the problems associated with such algorithmic systems, and how these constructions impact governance. Drawing on an example from the legal sphere, it highlights both the potential efficiency gains and the increasing tensions concerning automation and fairness. This article then adopts aspects of Carol Bacchi’s Foucauldian-inspired “What’s the Problem Represented to Be?” framework to trace how divergent problem framings, ranging from the United States’ emphasis on an “innovation gap,” to the European Union’s “trust deficit,” and China’s “stability risk,” have produced distinct regulatory trajectories. Yet, despite these divergent framings and national strategies, this article argues that a common post-2024 trend emerges, revealing a general shift toward regulatory softening, one that privileges innovation over precautionary safeguards. This convergence raises critical questions about the future direction and resilience of “algorithmic decision-making” governance.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".