The Penn State Center for Socially Responsible Artificial Intelligence Distinguished Lecture Series highlights world-renowned scholars of repute who have made fundamental contributions to the advancement of socially responsible artificial intelligence. The series aims to provoke attendees and participants to have thoughtful conversations and to facilitate discussion among students, faculty, and industry affiliates of the Center.
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“The Quest for Ethical Artificial Intelligence”
The Distributed Artificial Intelligence Research Institute (DAIR) was launched in December 2021 by Timnit Gebru as a space for independent, community-rooted AI research, free from Big Tech’s pervasive influence. Gebru believes that the harms embedded in AI technology are preventable and that when its production and deployment include diverse perspectives and deliberate processes, it can be put to work for people, rather than against them. With DAIR, Gebru aims to create an environment that is independent from the structures and systems that incentivize profit over ethics and individual well-being. In this talk, Gebru will discuss why she founded DAIR and what she hopes this interdisciplinary, community-based, global network of AI researchers can accomplish.
About the Speaker:
Timnit Gebru is the founder and executive director of the Distributed Artificial Intelligence Research Institute (DAIR). Prior to that she was fired by Google in December 2020 for raising issues of discrimination in the workplace, where she was serving as co-lead of the Ethical AI research team. She received her PhD from Stanford University, and did a postdoc at Microsoft Research, New York City in the FATE (Fairness Accountability Transparency and Ethics in AI) group, where she studied algorithmic bias and the ethical implications underlying projects aiming to gain insights from data. Gebru also co-founded Black in AI, a nonprofit that works to increase the presence, inclusion, visibility and health of Black people in the field of AI, and is on the board of AddisCoder, a nonprofit dedicated to teaching algorithms and computer programming to Ethiopian high school students, free of charge.
About the Series:
This event is hosted as part of the Penn State Center for Socially Responsible Artificial Intelligence's Distinguished Lecture Series in partnership with the College of Information Sciences and Technology. The series highlights world-renowned scholars of repute who have made fundamental contributions to the advancement of socially responsible artificial intelligence. The series aims to provoke attendees and participants to have thoughtful conversations and to facilitate discussion among students, faculty, and industry affiliates of the Center.
“Exploring the Power of Taxonomy and Embedding in Text Mining”
The real-world big data are largely dynamic, interconnected and unstructured text. It is highly desirable to transform such massive unstructured data into structured knowledge. Many researchers rely on labor-intensive labeling and curation to extract knowledge from text data. Such approaches, unfortunately, may not be scalable, especially when such texts are domain-specific and nonstandard (such as social media). We envision that massive text data itself may disclose a large body of hidden structures and knowledge. Equipped with domain-independent and domain-dependent knowledge-bases, we can explore the power of massive data to transform unstructured data into structured knowledge. In this talk, we introduce a set of methods developed recently in our group for such an exploration, including joint spherical text embedding, discriminative topic mining, taxonomy construction, and taxonomy-guided knowledge mining. We show that data-driven approach could be promising at transforming massive text data into structured knowledge.
About the Speaker:
Jiawei Han is the Michael Aiken Chair Professor in the Department of Computer Science, University of Illinois at Urbana-Champaign. He received the ACM SIGKDD Innovation Award (2004), IEEE Computer Society Technical Achievement Award (2005), IEEE Computer Society W. Wallace McDowell Award (2009), and Japan's Funai Achievement Award (2018). He is a Fellow of ACM and Fellow of IEEE and served as the Director of Information Network Academic Research Center (INARC) (2009-2016) supported by the Network Science-Collaborative Technology Alliance (NS-CTA) program of the U.S. Army Research Lab, and co-Director of KnowEnG, a Center of Excellence in Big Data Computing (2014-2019), funded by the NIH Big Data to Knowledge (BD2K) Initiative.
“Network Heterogeneity on Graph Neural Networks”
Graph neural network (GNN), as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. Basically, the current GNNs follow the message-passing framework which receives messages from neighbors and applies neural network to learn node representations. However, previous GNNs mainly focus on homogeneous graph, while in reality, the real-world graphs usually are far from homogeneity. Here we first examine the various types of network heterogeneity, including node and link type heterogeneity, neighborhood heterogeneity, fragment heterogeneity, temporal heterogeneity, and structure heterogeneity. We then discuss the implications and methods to overcome these heterogeneities.
About the Speaker:
Dr. Philip S. Yu is a Distinguished Professor and the Wexler Chair in Information Technology at the Department of Computer Science, University of Illinois at Chicago. Before joining UIC, he was at the IBM Watson Research Center, where he built a world-renowned data mining and database department. He is a Fellow of the ACM and IEEE. Dr. Yu is the recipient of ACM SIGKDD 2016 Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data, the IEEE Computer Society’s 2013 Technical Achievement Award for “pioneering and fundamentally innovative contributions to the scalable indexing, querying, searching, mining and anonymization of big data” and the Research Contributions Award from IEEE Intl. Conference on Data Mining (ICDM) in 2003 for his pioneering contributions to the field of data mining. Dr. Yu has published more than 1,300 referred conference and journal papers cited more than 138,000 times with an H-index of 172. He has applied for more than 300 patents. Dr. Yu was the Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data (2011-2017) and IEEE Transactions on Knowledge and Data Engineering (2001-2004).