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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:


    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-a6gKPUh7r3g

    Location: 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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