Description
Title: s Trustworthy AI Compatible with Frugality in Machine Learning?
Abstract: This presentation focuses on adversarial robustness in critical systems and its compatibility with frugality. We will examine the benefits, as well as the theoretical and practical limitations, of current adversarial robustness guarantees. Furthermore, we will explore how these guarantees interact with other desirable properties of trustworthy AI, such as explainability, privacy, and calibration, while highlighting their limitations in low-data regimes.
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