[IT1] Visual Analytics for Explainable Machine Learning

Machine learning has demonstrated being highly successful at solving many real-world applications ranging from information retrieval, data mining, and speech recognition, to computer graphics, visualization, and human-computer interaction. However, most users often treat the machine learning model as a “black box” because of its incomprehensible functions and unclear working mechanism. Without a clear understanding of how and why the model works, the development of high-performance models typically relies on a time-consuming trial-and-error procedure. This talk presents the major challenges explainable machine learning and exemplifies the solutions with several visual analytics techniques and examples, including data quality diagnosis, model understanding and diagnosis.

Shixia Liu is a professor at Tsinghua University. Her research interests include explainable machine learning, visual text analytics, and text mining. Shixia was elevated to an IEEE Fellow in 2021 and induced into IEEE Visualization Academy in 2020. She is an associate editor-in-chief of IEEE Transactions on Visualization and Computer Graphics and is an associate editor of Artificial Intelligence, IEEE Transactions on Big Data, and ACM Transactions on Intelligent Systems and Technology. She was one of the Papers Co-Chairs of IEEE VIS (VAST) 2016 and 2017 and is in the steering committee of IEEE VIS (2020-2023).