Director: Professor Matthew Lease
Overview: Our research spans artificial Intelligence (AI) modeling and human-computer interaction (HCI) design. We create novel datasets, build AI models, and evaluate model performance and end-user impacts. When automated AI falls short, we design human-in-the-loop approaches supported by AI model explanations and creative user interfaces. To promote fair AI, we design better ways to annotate data without bias and modeling techniques to mitigate dataset biases. We conduct fundamental research, applied to real-world problems that matter, as part of UT Austin's Good Systems Grand Challenge to design responsible AI technologies. A theme of ongoing work is content moderation: automated, human-in-the-loop, and human-safe practices to curb disinformation, hate speech, and polarization online.
Selected Research & Demos
Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga, Ligong Han, Min Kyung Lee, Wei Sun and Matthew Lease. Robust Human-AI Collaboration with Bandit Feedback. The Conference on Information Systems and Technology (CIST), 2022. Best Student Paper award. [conference-website]
Li Shi, Nilavra Bhattacharya, Anubrata Das, Matthew Lease, and Jacek Gwizdka. The Effects of Interactive AI Design on User Behavior: An Eye-tracking Study of Fact-checking COVID-19 Claims. In Proceedings of the 7th ACM SIGIR Conference on Human Information, Interaction and Retrieval (CHIIR), pages 315--320, 2022. [ bib | pdf | demo | sourcecode | video | poster | tech-report ]
Anubrata Das, Brandon Dang, and Matthew Lease. Fast, Accurate, and Healthier: Interactive Blurring Helps Moderators Reduce Exposure to Harmful Content. In Proceedings of the 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP), pages 33--42, 2020. [ bib | pdf | demo | blog-post | sourcecode | video | slides ]
Mucahid Kutlu, Tyler McDonnell, Tamer Elsayed, and Matthew Lease. Annotator Rationales for Labeling Tasks in Crowdsourcing. Journal of Artificial Intelligence Research (JAIR), 69:143--189, 2020. Award Winning Papers Track. [ bib | pdf | blog-post | data | conference-website ]
Soumyajit Gupta, Mucahid Kutlu, Vivek Khetan, and Matthew Lease. Correlation, Prediction and Ranking of Evaluation Metrics in Information Retrieval. In Proceedings of the 41st European Conference on Information Retrieval (ECIR), pages 636--651, 2019. Best Student Paper award. [ news | bib | pdf | data | sourcecode | slides | tech-report ]
An Thanh Nguyen, Aditya Kharosekar, Aditya Kharosekar, Saumyaa Krishnan,
Siddhesh Krishnan, Elizabeth Tate, Byron C. Wallace, and Matthew Lease.
Believe it or not: Designing a Human-AI Partnership for
In Proceedings of the 31st ACM User Interface Software and
Technology Symposium (UIST), pages 189--199, 2018.
[ bib |
Tyler McDonnell, Matthew Lease, Mucahid Kutlu, and Tamer Elsayed. Why Is That Relevant? Collecting Annotator Rationales for Relevance Judgments. In Proceedings of the 4th AAAI Conference on Human Computation and Crowdsourcing (HCOMP), pages 139--148, 2016. Best Paper Award. [ news | bib | pdf | blog-post | data | slides ]
Hyun Joon Jung and Matthew Lease. A Discriminative Approach to Predicting Assessor Accuracy. In Proceedings of the 37th European Conference on Information Retrieval (ECIR), pages 159--171, 2015. Samsung Human-Tech Paper Award: Silver Prize in Computer Science. [ bib | pdf | news ]
Join our Lab!
Current MSIS studentsEUREKA!. Independent study course credit is possible (e.g., in either informatics or computer science). Undergraduates have co-authored research papers with us in the past. Also see the CNS page on Undergraduate Research.
High School Students: apply for UT's Summer Research Academy