2021/11/29 by Mehrnoosh Askarpour, Alan Wassyng, Askarpour, Mehrnoosh +8
Computer Science · Decision Sciences · Engineering · Health Professions · #Adversarial Robustness in Machine Learning #Business #Computer science #Computer security #Engineering #FOS: Computer and information sciences #Machine Learning (cs.LG) #Occupational Health and Safety Research #Reliability engineering #Risk analysis (engineering) #Risk and Safety Analysis #Safety standards #cs.LG
paper · pdf · doi:10.48550/arxiv.2111.14324
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/11/29 · openalex publication_date 2021/11/29 · arxiv updated 2021/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Machine learning (ML) is finding its way into safety-critical systems (SCS). Current safety standards and practice were not designed to cope with ML techniques, and it is difficult to be confident that SCSs that contain ML components are safe. Our hypothesis was that there has been a rush to deploy ML techniques at the expense of a thorough examination as to whether the use of ML techniques introduces safety problems that we are not yet adequately able to detect and mitigate against. We thus conducted a targeted literature survey to determine the research effort that has been expended in applying ML to SCS compared with that spent on evaluating the safety of SCSs that deploy ML components. This paper presents the (surprising) results of the survey.