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Spring 2023

Babiker, Housam Khalifa Bashier

The recent success of deep neural networks has exposed the problem of model transparency. The need for explainability is particularly critical in sensitive domains. In addition, regulatory frameworks for the “responsible” deployment of AI are emerging, creating legal requirements for transparent,...

Fall 2024

Bashir, Zahra

Although the synthesis of programs encoding policies often carries the promise of interpretability, systematic evaluations were never performed to assess the interpretability of these policies, likely because of the complexity of such an evaluation. In this dissertation, we introduce a novel...

Fall 2023

Alateeq, Majed Mohammad

With the rapid development of machine learning models along with increasingly complex data structures, it becomes difficult to ground the reliability of models’ predictions despite the substantial progress in favor of high approximation properties. The lack of interpretability remains a key...