The Sotif Meta-Algorithm: Quantiative Analyses of the Safety of AI

<div class="section abstract"><div class="htmlview paragraph">This paper presents updates to a “meta-algorithm” for achieving safer AI driven systems by integrating systems theoretic process analysis, quantitative fault tree analysis, structured generation of safety metrics, and statistical hypothesis testing of metrics between simulation and reality. This paper presents updates to the meta-algorithm after its application in use cases involving commercial autonomous vehicle deployment.</div></div>

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Publication Details

Journal
SAE technical papers on CD-ROM/SAE technical paper series
Published
2025-09-16
DOI
https://doi.org/10.4271/2025-01-0437
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

The Sotif Meta-Algorithm: Quantiative Analyses of the Safety of AI

Neil Carlson, Michael Wagner, Chris Dwyer
SAE technical papers on CD-ROM/SAE technical paper series
Adversarial Robustness in Machine Learning
article

The Sotif Meta-Algorithm: Quantiative Analyses of the Safety of AI

Neil Carlson, Michael Wagner, Chris Dwyer
article en

Abstract

<div class="section abstract"><div class="htmlview paragraph">This paper presents updates to a “meta-algorithm” for achieving safer AI driven systems by integrating systems theoretic process analysis, quantitative fault tree analysis, structured generation of safety metrics, and statistical hypothesis testing of metrics between simulation and reality. This paper presents updates to the meta-algorithm after its application in use cases involving commercial autonomous vehicle deployment.</div></div>

SAE technical papers on CD-ROM/SAE technical paper seriesVol. 1
Edge Case Research (United States) (US)
Openalex Percentile: Top 87%
Adversarial Robustness in Machine Learning
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