The Riskification of Internal Auditors’ Ethical Deliberation: An Emerging Third Logic Between Norms and Values?
Bibliographic record
Abstract
Abstract What ethical challenges do internal auditors (IAs) encounter in their professional role, and how do they navigate these hurdles, especially when weaving risks into their ethical judgments? Anchored in philosophical concepts distinguishing norms from values, and the notion that risk is intrinsically moral, this research delves into interviews of 33 Canadian public sector IAs across various government strata. This primary data are enriched by insights from archival documents and an ethics training session attended by 11 internal audit executives. Our analysis reveals two primary ethical challenges faced by IAs—ethical issues and dilemmas—which unfold in the various contexts we explicate in this study. To address them, IAs tend to favor axiological logic (values driven) over deontological logic (norms driven). However, in some situations, a prudential logic centered on risk becomes their touchstone. Our key takeaways are threefold: (1) a highlight of the ethical quandaries IAs grapple with; (2) evidence that prudential logic, with its merits and flaws, is used to bridge the gap that sometimes exists between professional norms and individual values; and (3) an emphasis on the weak reliance of IAs on the Institute of Internal Auditors Code of Ethics, hinting at avenues for its enhanced outreach and pedagogy.
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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 | 0.053 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.108 |
| Scholarly communication | 0.030 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".