Perspectives on AI-ML Safety Assurance
Bibliographic record
Abstract
AI-ML suffers from a reliability glass-ceiling phenomenon (e.g.~10 -3 error/inference), making it incompatible with safety-criticality.Several orders of magnitude are missing.We explain why, we point to the characteristics of ML that conflict with the assurance objectives assigned to safety-critical developments.Could encapsulation of ML constituents into fault-tolerant architectures, ML development assurance, and software/hardware development assurance, altogether mitigate the gap?We argue that in spite of impressive progress of ML state-of-the-art, the answer is negative.Drawing from Topological Data Analysis (TDA) and set-based non-linear control, we propose to supplement ML point-based specification and verification with volume-based specification and verification to meet 10 -5 err./ inf.levels, as a minimum.We outline the rationale of a new research field we name (Ultra) Reliable Machine Learning, at the confluence of TDA, statistics on manifolds, and ML safety assurance.Some cross-domain safety regulation principles guide the underlying rationale.We illustrate the methodology on image classification.
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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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".