CS Colloquium - Bridging the Trustworthy Gap between AI and Humans: Interpretation Techniques for Modern NLP

CS Colloquium - Bridging the Trustworthy Gap between AI and Humans: Interpretation Techniques for Modern NLP promotional image


 Hanjie Chen


Neural network models have been pushing computers’ capacity limit on natural language understanding and generation while lacking interpretability. The black-box nature of deep neural networks hinders humans from understanding their predictions and trusting them in real-world applications.

In this talk, I will introduce my effort in bridging the trustworthy gap between models and humans by developing interpretation techniques, which cover three main phases of a model life cycle—training, testing, and debugging. I will demonstrate the critical values of integrating interpretability into every state of model development: (1) making model prediction behavior transparent and interpretable during training; (2) explaining and understanding model decision-making on each test example; (3) diagnosing and debugging models (e.g., robustness) based on interpretations. I will discuss future directions on incorporating interpretation techniques with system development and human interaction for long-term trustworthy AI.


Hanjie Chen is a PhD candidate in Computer Science at the University of Virginia. Her research interests lie in Trustworthy AI, Natural Language Processing (NLP), and Interpretable Machine Learning. She is a recipient of the Carlos and Esther Farrar Fellowship and the Best Poster Award at the ACM CAPWIC 2021. Her work has been published at top-tier NLP/AI conferences (e.g., ACL, AAAI, EMNLP, NAACL) and selected by the National Center for Women & Information Technology (NCWIT) Collegiate Award Finalist 2021. Besides, as the primary instructor, she co-designed and taught a cross-listed course, CS 4501/6501 Interpretable Machine Learning, at UVA. Her effort in teaching was recognized by the UVA CS Outstanding Graduate Teaching Award and University-wide Graduate Teaching Awards Nominee (top 5% of graduate instructors). 

Monday, February 27, 2023 4:00pm to 5:00pm
Pappajohn Business Building
21 East Market Street, Iowa City, IA 52245
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Individuals with disabilities are encouraged to attend all University of Iowa–sponsored events. If you are a person with a disability who requires a reasonable accommodation in order to participate in this program, please contact Computer Science Dept. in advance at 319-335-0713 or matthieu-biger@uiowa.edu.