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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,...
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Fall 2020
Motallebi Shabestari, Mohammad Hossein
Present-day advancements in AI, amongst other things, have often been regarding improving the accuracy of classification models. One lagging aspect, however, is justifying the decisions made by those models. Recently, AI researchers are paying more attention to fill this gap, leading to the...
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Spring 2019
While deep learning has proven to be a powerful new tool for modeling and predicting a wide variety of complex phenomena, those models remain incomprehensible black boxes. This is a critical impediment to the widespread deployment of deep learning technology, as decades of research have found...
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Spring 2023
With machine learning models becoming more complicated and more widely applied to solve real-world challenges, there comes the need to explain their reasoning. In parallel with the advancements of deep learning methods, Explainable AI (XAI) algorithms have been proposed to address the issue of...