Coupling algebraic topology theory, formal methods and safety requirements toward a new coverage metric for artificial intelligence models
Résumé
Safety requirements are among the main barriers to the industrialization of machine learning based on deep learning architectures. In this work, a new metric of data coverage is presented by exploring the algebraic topology theory and the abstract interpretation process. The algebraic topology connects the cloud points of the dataset and the abstract interpretation evaluates the robustness of the model. Thus, the coverage metric evaluates simultaneously the dataset and the robustness, and highlights safe and unsafe areas. We also propose the first complete process to evaluate, in terms of data completeness, the machine learning models by providing a workflow based on the proposed metric and a set of safety requirements applied on autonomous driving. The obtained results provide an interpretable coverage evaluation and a promising line of research in the industrialization of artificial intelligence models. It is important to mention that the proposed metric is not dependent on the specific data. In other terms, it can be applied on 1 to n-dimensional data.
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