The necessity of AI audit standards boards
Bibliographic record
Abstract
Abstract Auditing of AI systems is a promising way to understand and manage ethical problems and societal risks associated with contemporary AI systems, as well as some anticipated future risks. Efforts to develop standards for auditing artificial intelligence (AI) systems have therefore understandably gained momentum. However, current approaches are not just insufficient, but can be actively harmful. Transparency alone does not address concerns about risk. Internal auditing is insufficient, and easily becomes safety-washing. External audit is better, but requires credible standards. Industry-led approaches to building standards or to perform audits lack credibility and undermine other efforts. Regulation often is ill adapted and becomes a static barrier. Lastly, all of these limited technical, governance, and even ethical assessments fail to ensure continued stakeholder input and engagement. Instead, the paper proposes the establishment of an AI Audit Standards Board, in line with best practices in other fields, including safety-critical industries like aviation and nuclear energy, as well as more prosaic ones such as financial accounting and pharmaceuticals. This would address the evolving nature of AI technologies, help maintain public trust in AI, and promote a culture of safety and ethical responsibility within the AI industry. By ensuring audits remain relevant, robust, and responsive to the rapid advancements in AI, auditing AI will not devolve into safety washing and addresses risks and ethical concerns that will continue to arise as AI becomes increasingly important in society, and as human interaction with these systems changes over time.
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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 | 0.180 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".