We’ve all heard the saying: to build the next big tech product, we need to “move fast and break things.” But when it comes to building AI, does this come at a cost?
We’re excited to share u:wait, a new organization at Waterloo for undergraduates who care about algorithmic integrity in tech. We believe that good AI will come from moving slowly and building with care, asking questions about how our algorithms affect society.
u:wait and Waterloo.AI invite you to our first public forum on algorithmic integrity, bringing together student researchers, professors, and industry partners on March 30th, 2022 at 5:30PM. We invite all to discuss the question: is tech broken? And if so, who will fix it?
Sign up at: https://tinyurl.com/is-tech-broken.
Speaker Information
Vijay Ganesh
Dr. Vijay Ganesh is an associate professor at the University of Waterloo and the Co-Director of the Waterloo Artificial Intelligence Institute. Prior to joining Waterloo in 2012, he was a research scientist at MIT (2007-2012) and completed his PhD in computer science from Stanford in 2007. Vijay's primary area of research is the theory and practice of SAT/SMT solvers aimed at AI, software engineering, security, mathematics, and physics. In this context he has led the development of many SAT/SMT solvers, most notably, STP, Z3 string, MapleSAT, and MathCheck. He has also proved several decidability and complexity results in the context of first-order theories. He has won over 25 awards, honors, and medals to-date for his research, including an ACM Impact Paper Award at ISSTA 2019, ACM Test of Time Award at CCS 2016, and a Ten-Year Most Influential Paper citation at DATE 2008. He is the Editor-in-Chief of the Springer book series "Progress in Computer Science and Applied Logic" (PCSAL) and has co-chaired many conferences, workshops, and seminars including a Simons Institute semester @ Berkeley on Boolean Satisfiability in 2021.
Cameron Shelley
Cameron Shelley is a Lecturer at the Centre for Society, Technology & Values at the University of Waterloo. Dr. Shelley’s research interests include the philosophy of design, fairness in technological design, social impact of technology, and analogical reasoning. He has taught a variety of courses including Cities, Technology & Society, Design & Society, Biotechnology & Society, and Artificial Intelligence & Society.
Maura R. Grossman
Maura R. Grossman, J.D., Ph.D. is a Research Professor in the David R. Cheriton School of Computer Science at the University of Waterloo, an Adjunct Professor at Osgoode Hall Law School of York University, and an affiliate faculty member at the Vector Institute, all in Ontario, Canada. She also is Principal of Maura Grossman Law, an eDiscovery law and consulting firm in Buffalo, New York. Maura is most well known for her scholarly work on technology-assisted review (“TAR”), which has been widely cited in the case law, both in the U.S. and abroad, and for her appointments as a special master or expert in multiple high-profile U.S. federal and state court cases.
Ricardo Baeza-Yates
Ricardo Baeza-Yates is Director of Research at the Institute for Experiential AI of Northeastern University. Before he was the CTO of NTENT, a semantic search technology company based in California and prior to this role, he was VP of Research at Yahoo Labs, based in Barcelona, Spain, and later in Sunnyvale, California, from 2006 to 2016. He is co-author of the best-seller Modern Information Retrieval textbook published by Addison-Wesley in 1999 and 2011 (2nd ed), that won the ASIST 2012 Book of the Year award. In 2007 he won the Graham Medal given to distinguished Waterloo alumni. From 2002 to 2004 he was elected to the Board of Governors of the IEEE Computer Society and between 2012 and 2016 was elected to the ACM Council. Since 2010 is a founding member of the Chilean Academy of Engineering. In 2009 he was named ACM Fellow and in 2011 IEEE Fellow, among other awards and distinctions. He obtained a Ph.D. in CS from the University of Waterloo, Canada, in 1989, and his areas of expertise are web search and data mining, information retrieval, bias and ethics on AI, data science and algorithms in general.
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