Abstract
Assuring the safety of systems that use Artificial Intelligence (AI), specifically Machine Learning (ML) components, is difficult because of the unique challenges that AI presents for current assurance practice. However, what is also missing is an overall understanding of this multi-disciplinary problem space. In this paper, a model is given that frames the challenges into three categories which are aligned to the reasons why they occur. Armed with a common picture of where existing issues and solutions “fit-in”, the aim is to help bridge cross-domain conceptual gaps and provide a clearer understanding to safety practitioners, ML experts, regulators and anyone involved in the assurance of a system with AI.
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CITATION STYLE
Fang, X., & Johnson, N. (2019). Three Reasons Why: Framing the Challenges of Assuring AI. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11699 LNCS, pp. 281–287). Springer Verlag. https://doi.org/10.1007/978-3-030-26250-1_22
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