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CAISAR: A platform for Characterizing Artificial Intelligence Safety and Robustness

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arxiv 2206.03044 v2 pith:4WN3TBW7 submitted 2022-06-07 cs.AI cs.LOcs.NEcs.SE

classification cs.AIcs.LOcs.NEcs.SE
keywords caisarplatformverificationhandproblemsrobustnesssafetytools
verification ladder T0 review T1 audit T2 compute T3 formal

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We present CAISAR, an open-source platform under active development for the characterization of AI systems' robustness and safety. CAISAR provides a unified entry point for defining verification problems by using WhyML, the mature and expressive language of the Why3 verification platform. Moreover, CAISAR orchestrates and composes state-of-the-art machine learning verification tools which, individually, are not able to efficiently handle all problems but, collectively, can cover a growing number of properties. Our aim is to assist, on the one hand, the V\&V process by reducing the burden of choosing the methodology tailored to a given verification problem, and on the other hand the tools developers by factorizing useful features-visualization, report generation, property description-in one platform. CAISAR will soon be available at https://git.frama-c.com/pub/caisar.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 2 citations worldwide. Full citation record

  1. Proof-Carrying Neuro-Symbolic Code

    cs.PL 2025-04 conditional novelty 4.0 of 10

    The paper defines proof-carrying neuro-symbolic code, a research program for delivering neural-network-containing software with formal safety proofs, and reviews early tools and challenges.

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