Regulating Artificial Intelligence for CANDU Software Qualifications
Bibliographic record
Abstract
This paper examines the inadequacies in qualifying Artificial Intelligence (AI) software for the Canadian nuclear energy sector. The nuclear energy sector is a high-risk environment with strict regulations to ensure safety. Despite the rising popularity of new technologies like AI, a compliance assessment would be needed against nuclear qualification procedures. First, the paper analyzes the existing regulatory framework within the Canadian nuclear sector. This analysis reveals potential gaps that traditional software qualification methods fail to address when applied to AI. The risks of AI, primarily linked to the complexity and opacity of decision-making processes, show the need for a new approach to AI regulation in nuclear. Next, a review of the Canadian regulatory framework focusing on the Canadian Standards Association (CSA) N290.14 with case studies of qualifying commercial software is presented to showcase the software qualification lifecycle. Through these detailed case studies, gaps are identified when applying the software qualifications methods to AI software. The paper presents three proposed methodologies for future AI qualification: model interpretability, feature importance, and data variety. These methodologies are investigated in order to improve the transparency, reliability, and safety of AI applications in high-risk contexts such as nuclear power plants. Finally, the paper proposes incorporating these three methodologies of evaluating AI complexity and reliability into the software qualification framework to significantly mitigate the risks and support safe deployment and operations of AI based software in the nuclear sector.
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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 | 0.050 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".