How to certify machine learning based safety-critical systems? A systematic literature review

Context Machine Learning (ML) has been at the heart of many innovations over the past years. However, including it in so-called “safety-critical” systems such as automotive or aeronautic has proven to be very challenging, since the shift in paradigm that ML brings completely changes traditional cert...

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Vydáno v:Automated software engineering Ročník 29; číslo 2; s. 38
Hlavní autoři: Tambon, Florian, Laberge, Gabriel, An, Le, Nikanjam, Amin, Mindom, Paulina Stevia Nouwou, Pequignot, Yann, Khomh, Foutse, Antoniol, Giulio, Merlo, Ettore, Laviolette, François
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.11.2022
Springer Nature B.V
Springer Verlag
Témata:
Artificial Intelligence
Certification
Computer Science
Literature reviews
Machine learning
Safety critical
Software Engineering/Programming and Operating Systems
Systematic review
Safety-critical
Certification
Machine learning
Systematic literature review
ISSN:0928-8910, 1573-7535
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Shrnutí:Context Machine Learning (ML) has been at the heart of many innovations over the past years. However, including it in so-called “safety-critical” systems such as automotive or aeronautic has proven to be very challenging, since the shift in paradigm that ML brings completely changes traditional certification approaches. Objective This paper aims to elucidate challenges related to the certification of ML-based safety-critical systems, as well as the solutions that are proposed in the literature to tackle them, answering the question “How to Certify Machine Learning Based Safety-critical Systems?”. Method We conduct a Systematic Literature Review (SLR) of research papers published between 2015 and 2020, covering topics related to the certification of ML systems. In total, we identified 217 papers covering topics considered to be the main pillars of ML certification: Robustness , Uncertainty , Explainability , Verification , Safe Reinforcement Learning , and Direct Certification . We analyzed the main trends and problems of each sub-field and provided summaries of the papers extracted. Results The SLR results highlighted the enthusiasm of the community for this subject, as well as the lack of diversity in terms of datasets and type of ML models. It also emphasized the need to further develop connections between academia and industries to deepen the domain study. Finally, it also illustrated the necessity to build connections between the above mentioned main pillars that are for now mainly studied separately. Conclusion We highlighted current efforts deployed to enable the certification of ML based software systems, and discuss some future research directions.
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ISSN:0928-8910
1573-7535
DOI:10.1007/s10515-022-00337-x