Reshaping the Space of Ethics for Expert Work: Deliberative Approaches for Deploying Artificial Intelligence in Auditing
Bibliographic record
Abstract
Abstract This paper investigates what drives collaboration or conflict between domain and method experts in deploying Artificial Intelligence (AI), and how these dynamics can be managed to fairly reshape the space of ethics guiding expert work. The emergence of sophisticated epistemic technologies, e.g., AI, has amplified the importance of method experts. Leveraging their knowledge of data science, mathematics, and coding, method experts have challenged the authority of domain experts, like external auditors. These challenges generate tensions extending beyond jurisdictional and operational control to profound ethical and moral concerns, since jurisdictional authority determines whose values are embedded in AI-driven decision-making. Empirical research presents divergent views on how domain and method experts navigate such tensions in AI deployments, with some studies suggesting that domain experts leverage their status and power to resist changes to ethical considerations. To reconcile these fragmented perspectives, we develop a conceptual framework grounded in the literature that frames expert work as a balance between individual craftsmanship and collective judgment. We offer two contributions. First, drawing on foundational literature on technology and expert work as well as empirical studies on AI and external auditing, we identify existing conditions and mechanisms that foster cooperation or conflict between different expert groups. Second, informed by Habermasian conceptions of deliberative democracy, we explicate how the space of ethics in deploying AI can be fairly reshaped through multi-layered deliberations that account for diverse stakeholder interests. Our framework provides a foundation for future empirical studies to deepen understanding of AI’s impact on expert work and ethics.
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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 | 0.078 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.085 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".