Through the recent developments of AI, the use of models produced by machine learning has become widespread, even in industrial settings. However, studies are flourishing
showing the dangers that such models can bring, in terms of safety, privacy or even
fairness. We can for example cite the adversarial attacks, small perturbations invisible
to naked eyes which can drastically change the output of our AI. To face these dangers
and improve trust in AI, research works are constantly emerging, resulting into several
tools like ERAN (Gehr et al. 2018) or Marabou (Katz et al. 2019).
In the past couple of years, the AISER team has developed CAISAR (Alberti et al. 2022), an open-source platform that focuses on the specification and verification of AI systems’
robustness and safety. In particular, CAISAR provides a language for formally specifying
AI properties, and integrates various state-of-the-art tools for establishing their validity
in a guaranteed way.

The concept of a confidence-based safety property has been recently introduced by
Athavale et al. (Athavale et al. 2024) to recast robustness and fairness properties in
terms of the confidence score with which a neural network generates its outcomes.
The main objective of this internship will be to investigate, design, and implement a
support for confidence-based safety properties in the CAISAR, the AISER’s open-source
platform for characterizing AI systems’ safety and robustness.

The broad internship goals are:
• familiarization with the state-of-the-art on formal approaches to properties for AI
safety (Casadio et al. 2022)
• familiarization with the work on confidence-based safety properties (Athavale et
al. 2024)
• getting started with the CAISAR platform
• design and implementation of confidence-based safety properties in CAISAR
• identification and evaluation against benchmarks