Matthew Lease

Biography

Matthew Lease is a Professor in the School of Information at the University of Texas at Austin, a Distinguished Member of the Association for Computing Machinery (ACM), a Senior Member of the Association for the Advancement of Artificial Intelligence (AAAI), and an Amazon Scholar. He holds degrees in Computer Science from the University of Washington (B.Sc.) and Brown University (M.Sc., Ph.D.). Lease has received Early Career awards from three major U.S. agencies: the Defense Advanced Research Projects Agency (DARPA), the National Science Foundation (NSF), and the Institute for Museum and Library Sciences (IMLS).

Lease serves as a faculty founder and leader of UT’s Good Systems, an eight-year, $20M university-wide Grand Challenge aimed at designing responsible AI technologies. His recent recognitions include the 2024 Inaugural Test of Time Paper Award at the AAAI Conference on Human Computation and Crowdsourcing (HCOMP), awarded for papers from 2013-2014, and the 2024 Most Influential Paper Award at the IEEE/ACM International Conference on Automated Software Engineering (ASE), awarded for papers from 2011-2013. Additional recent accolades include Best Student Paper at the 2022 Conference on Information Systems and Technology (CIST), the Conference Award Track in the Journal of Artificial Intelligence Research (JAIR, 2020), and Best Student Paper at the 2019 European Conference for Information Retrieval (ECIR). In 2023-2024, Lease was invited four times to address the Texas Legislature on responsible AI.

Lease directs the UT Austin Laboratory for Artificial Intelligence and Human-Centered Computing (AI&HCC), where his team’s research spans artificial intelligence (AI) modeling and human-computer interaction (HCI) design. The lab creates novel datasets, builds AI models, and evaluates both model performance and their impact on end-users. When automated AI falls short, they design human-in-the-loop approaches, leveraging AI model explanations and creative user interfaces. To promote fair AI, the lab focuses on better annotation techniques to avoid bias and develops modeling strategies to mitigate dataset biases. Their work tackles real-world problems as part of UT Austin's Good Systems Grand Challenge, with an ongoing emphasis on content moderation—exploring automated, human-in-the-loop, and human-safe practices to combat disinformation, hate speech, and online polarization.

Laboratory for Artificial Intelligence and Human-Centered Computing (AI&HCC)

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