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Conferences, Lectures, & Seminars
Events for November
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CAIS Seminar: Swabha Swayamdipta (USC) - Contextualizing Bias in Hate Speech Detection through Annotator Perspectives
Wed, Nov 09, 2022 @ 02:00 AM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Swabha Swayamdipta, University of Southern California
Talk Title: Contextualizing Bias in Hate Speech Detection through Annotator Perspectives
Series: USC Center for Artificial Intelligence in Society (CAIS) Seminar Series
Abstract: In an increasingly online world, content moderation in social media has become immensely important. However, existing hate speech detection systems are riddled with racial biases introduced during annotation, which are reinforced and propagated by models trained on such data. In this talk, I will first present the inadequacies of current methods for debiasing hate speech detection. I will show how the subjectivity of this task design leads to debiasing failures. Next, I will focus on uncovering the origin of bias in toxic language detection. I will demonstrate how annotators' demographics and beliefs influence their toxicity ratings, and how ignoring such societal context can lead to biased outcomes. Overall, I will argue for the value of rethinking traditional the hate speech classification task, and the need for richer context in hate speech datasets.
Prof. Swayamdipta will give her talk in person at GFS 116 and we will also host the talk over Zoom.
Register in advance for this webinar at:
https://usc.zoom.us/webinar/register/WN_50yG4RHVTa-a6gKPUh7r3g
After registering, attendees will receive a confirmation email containing information about joining the webinar.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Swabha Swayamdipta is an Assistant Professor of Computer Science and a Gabilan Assistant Professor at the University of Southern California. Her research interests are in natural language processing and machine learning, with a primary interest in the estimation of dataset quality, the semi-automatic collection of impactful data, and evaluating how human biases affect dataset construction and model decisions. At USC, Swabha leads the Data, Interpretability, Language and Learning (DILL) Lab. She received her PhD from Carnegie Mellon University, and was then a postdoc at the Allen Institute for AI. Her work has received outstanding paper awards at ICML 2022 and NeurIPS 2021 as well as an honorable mention for the best overall paper at ACL 2020.
Host: USC Center for Artificial Intelligence in Society (CAIS)
Webcast: https://usc.zoom.us/webinar/register/WN_50yG4RHVTa-a6gKPUh7r3gLocation: Grace Ford Salvatori Hall Of Letters, Arts & Sciences (GFS) - 116
WebCast Link: https://usc.zoom.us/webinar/register/WN_50yG4RHVTa-a6gKPUh7r3g
Audiences: Everyone Is Invited
Contact: Department of Computer Science
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Dean's Seminar: Dean Gregory D. Abowd (Northeastern University) - The Internet of Materials: Rethinking the future of computing
Thu, Nov 10, 2022 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dean Gregory D. Abowd, Northeastern University
Talk Title: The Internet of Materials: Rethinking the future of computing
Series: Dean's Seminar
Abstract: If we trace how computers have evolved over the past 8 or so decades, we can certainly see the impact of increasingly sophisticated manufacturing techniques. Computers now come in many different shapes and sizes. And applications, of course, have driven the widespread adoption, so much so that it appears we have an insatiable appetite for computing, and the power that is needed to feed it. That's a problem. We must take more seriously some of the past assumptions of how we manufacture computers and what properties the constituent materials impose. In this talk, I will introduce the notion of the Internet of Materials, whereby the power, form factor, and manufacturing costs of a computational object take precedence over other functional features of that object. I will show some simple examples that highlight how we can create self-sustaining computational materials. The purpose of the talk is to motivate researchers to think creatively about the convergence of materials, manufacturing, and computing. I hope these initial, and somewhat simple, examples prompt deeper discussions on how Northeastern can become a leader in defining a complementary computing industry.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Gregory D. Abowd is the Dean of the College of Engineering at Northeastern University, where he is also a Professor of Electrical and Computer Engineering with affiliate appointments in the Khoury College of Computer Sciences and the Bouvé College of Health Sciences. Prior to joining Northeastern in March 2021, Dr. Abowd was faculty in the College of Computing at the Georgia Institute of Technology for over 26 years, where he held the titles of Regents' Professor and J.Z. Liang Endowed Chair in the School in Interactive Computing. His research falls largely in the area of Human-Computer Interaction with an emphasis on applications and technology development for mobile and ubiquitous computing in everyday settings. His research has introduced innovations in the classroom, the home, for stakeholders connected with autism, and sustainable forms of computing in everyday life. He has been the founding Editor-in-Chief for two major journals and is the most highly cited researcher in HCI and ubiquitous computing in the world, according to csrankings.org (the second two are both his former PhD students). Dr. Abowd is a Fellow of the ACM and an elected member of the ACM SIGCHI Academy. He was a 2009 recipient of the ACM Eugene Lawler Humanitarian Contributions within Computer Science and Informatics. He earned his Bachelor of Science in Honors Mathematics (summa cum laude) from the University of Notre Dame in 1986 as well as a Master of Science (1987) and Doctor of Philosophy (1991) in Computation from the University of Oxford, where he attended as a Rhodes Scholar.
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Department of Computer Science
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Machine Learning Center Seminar: Yang Liu (UC Santa Cruz) - Agency Bias in Machine Learning
Thu, Nov 17, 2022 @ 10:00 AM - 11:30 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Yang Liu, UC Santa Cruz
Talk Title: Agency Bias in Machine Learning
Series: Machine Learning Seminar Series
Abstract: A trained machine learning model (e.g., a classifier) will ultimately observe data generated according to agents' responses. For instance, the rising literature on strategic classification concerns the setting where agents are fully rational and can best respond to a classifier in their own interests. The above interaction will lead to a distribution shift between training and deployment and will challenge the existing performance and fairness guarantees of the trained model. In this talk, I'll discuss three types of agency bias that arise due to the above interactional effects between agents and machine learning models. I'll then go over possible mitigation efforts, including our very recent works on certifying the fairness guarantees on an unknown and possibly different deployment distribution.
References:
[1] Unfairness Despite Awareness: Group-Fair Classification with Strategic Agents. Andrew Estornell, Sanmay Das, Yang Liu and Yevgeniy Vorobeychik. Preprint, 2022.
[2] Actionable Recourse in Linear Classification. Berk Ustun, Alexander Spangher and Yang Liu
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT), 2019.
[3] Unintended Selection: Persistent Qualification Rate Disparities and Interventions. Reilly Raab and Yang Liu. Neural Information Processing Systems (NeurIPS), 2021.
[4] Fairness Transferability Subject to Bounded Distribution Shift. Yatong Chen, Reilly Raab, Jialu Wang and Yang Liu. Neural Information Processing Systems (NeurIPS), 2022.
Prof. Liu will give his talk in person at EEB 248 and we will also host the talk over Zoom.
Register in advance for this webinar at:
https://usc.zoom.us/webinar/register/WN_WtHgpFUFSbCI214E2i9q3Q
After registering, attendees will receive a confirmation email containing information about joining the webinar.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Yang Liu is currently an Assistant Professor of Computer Science and Engineering at UC Santa Cruz (2018 - present). He was previously a postdoctoral fellow at Harvard University (2016 - 2018). He obtained his Ph.D. degree from the Department of EECS, University of Michigan, Ann Arbor in 2015. He is interested in weakly supervised learning and algorithmic fairness. He is a recipient of the NSF CAREER Award and the NSF Fairness in AI award (lead PI). He has been selected to participate in several high-profile projects, including DARPA SCORE and IARPA HFC. His recent works have won four best paper awards at relevant workshops.
Host: Yan Liu
Webcast: https://usc.zoom.us/webinar/register/WN_WtHgpFUFSbCI214E2i9q3QLocation: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/webinar/register/WN_WtHgpFUFSbCI214E2i9q3Q
Audiences: Everyone Is Invited
Contact: Department of Computer Science
