Caisar: A platform for characterizing artificial intelligence safety and robustness

Since the third AI revolution in 2012, industry displayed a keen interest in the newfound
capabilities of machine learning. However, in the field of critical systems, existing regulations
and practices require some degree of formal specification (and verification). Furthermore,
machine learning specification is implicitly defined by hyperparameters that are impossible
to formalise (the dataset, the architecture, the objective function, the intended goal). To
address those newfound challenges and fulfill its mission to support industrial actors, the
French Atomic Energy Commission develop and maintain several tools for the specification
and verification of machine learning systems. For seven years, those tools were applied in
industrial settings, in national and international projects. Those applications informed new research directions that we will overview in this talk.