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Project

Responsibly Uncertain: Quantifying the Robustness of Responsible AI in the Face of Uncertainty (RespUncible)

Aasa Feragen - Professor, Department and Administering Institution: Technical University of Denmark, Department of Applied Mathematics and Computer Science
Grant amount: DKK 10,999,638

The upcoming AI act will likely lead to widespread use of trustworthy AI methods such as explainability and algorithmic fairness. Such methods are crucial to ensure that AI is implemented responsibly and safely into increasingly critical societal functions, such as medical imaging. Nevertheless, the reliability of explanations and algorithmic fairness models is rarely addressed in state-of-the-art responsible AI research. In this project, we will showcase how trustworthy AI algorithms can fail, and develop theoretical and practical links between uncertainty in AI models, and failure modes of their trustworthy counterparts.

This has several advantages: First, it gives us potential tools to assess whether trustworthy AI algorithms are likely to fail, so that we can safely use them when they don’t. Second, these methods come with a straightforward generalization to the modern generative AI models, for whom trustworthy AI tools are currently largely unavailable.

Project participants
Aasa Feragen
Professor, Department and Administering Institution: Technical University of Denmark, Department of Applied Mathematics and Computer Science