Eliminative Argumentation for Arguing System Safety - A Practitioner’s Experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Safety cases are an essential artifact for establishing the safety of complex systems. Industrial use of safety cases varies between industries. Due to inconsistent regulatory guidance, numerous different strategies, notations, and techniques have been developed for safety case construction. Eliminative Argumentation (EA) has been proposed as a technique to systematically improve confidence in a safety case via `defeasible reasoning' wherein reasons to doubt safety claims are introduced and subsequently eliminated. Elimination of doubt results in increased confidence. This paper reports on the application of EA to seven different software-intensive systems in the automotive, rail, and industrial control industries. Our experiences suggest that EA's doubt-driven approach to safety argumentation increases confidence in a safety case and can be used to support activities such as independent safety assessments and safety verification and validation. From our experiences we synthesized into a set of lessons learned.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it