On Assessing ML Model Robustness: A Methodological Framework (Academic Track)

🤖 Plain-English Summary

Due to their uncertainty and vulnerability to adversarial attacks, machine learning (ML) models can lead to severe consequences, including the loss of human life, when embedded in safety-critical systems such as autonomous vehicles. The framework encompasses methodological processes (guidelines) captured in Capella models, along with a set of supporting tools.

🔑 Key Findings

• Therefore, it is crucial to assess the empirical robustness of such models before integrating them into these systems.
• ML model robustness refers to the ability of an ML model to be insensitive to input perturbations and maintain its performance.
• Against this background, the Confiance.ai research program proposes a methodological framework for assessing the empirical robustness of ML models.

🔗 Related Resources