CS Events

Qualifying Exam

Counterfactual Explainable AI


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Wednesday, May 25, 2022, 10:00am - 11:30pm


Zoom Info: https://us06web.zoom.us/j/86425295474?pwd=L2NzYk1FOVF5cHNlbnpKWEFRV25CUT09

Meeting ID: 864 2529 5474

Passcode: 648534

Speaker: Juntao Tan

Location : Virtual


Professor Yongfeng Zhang (Advisor)

Professor Desheng Zhang

Professor Dong Deng

Professor Jie Gao

Event Type: Qualifying Exam

Abstract: By providing explanations for users and AI system designers to facilitate better decision making and system understanding, explainable AI has been an important research problem. It is challenging because the most state-of-the-art machine learning models, which utilize deep neural networks, are non-transparent. In my research on Counterfactual Explainable AI, we take insights of counterfactual reasoning from causal inference to address this challenge. We first mathematically formulate the complexity and strength of explanations, and then propose a general model-agnostic counterfactual learning framework to seek simple (low complexity) and effective (high strength) explanations for the model decision. More specifically, counterfactual reasoning asks, “if A did not happen, will B happen?”. When applied in the machine learning field, it looks for a minimal change on the input such that the prediction will be different. Therefore, the changed factors are crucial for the original prediction made by the system, which constitute the counterfactual explanations. We formulate this idea as a machine learning optimization problem and generate faithful explanations. Meanwhile, we also design metrics based on counterfactual reasoning to quantitatively evaluate the necessity and sufficiency of the explanations. They are proved to be suitable for evaluating generated explanations in explainable AI, which is a challenging task in this field. We conduct a comprehensive set of experiments for different machine learning applications, such as recommendations and graph-based drug mutagenicity predictions, to show the effectiveness of the proposed counterfactual-based explainable models as well as the evaluation metrics.


Rutgers University School of Arts & Sciences

Contact  Yongfeng Zhang