When: Sep 22 2020 @ 11:00 AM

Location
Zoom: see link below in abstract
Abstract
https://jhuapl.zoomgov.com/j/1619432368?pwd=UkRNNFBKODVoYThRZEJpNVo4b1N2Zz09
Recent years have seen an astounding growth in deployment of AI systems in critical domains such as autonomous
vehicles, criminal justice, healthcare, hiring, housing, human resource management, law enforcement, and public
safety, where decisions taken by AI agents directly impact human lives. Consequently, there is an increasing
concern if these decisions can be trusted to be correct, reliable, fair, and safe, especially under adversarial
attacks. How then can we deliver on the promise of the benefits of AI but address these scenarios that have lifecritical consequences for people and society? In short, how can we achieve trustworthy AI? Under the umbrella
of trustworthy computing, there is a long-established framework employing formal methods and verification
techniques for ensuring trust properties like reliability, security, and privacy of traditional software and hardware
systems. Just as for trustworthy computing, formal verification could be an effective approach for building trust in
AI-based systems. However, the set of properties needs to be extended beyond reliability, security, and privacy to
include fairness, robustness, probabilistic accuracy under uncertainty, and other properties yet to be identified and
defined. Further, there is a need for new property specifications and verification techniques to handle new kinds of
artifacts, e.g., data distributions, probabilistic programs, and machine learning based models that may learn and
adapt automatically over time. This talk will pose a new research agenda, from a formal methods perspective, for us
to increase trust in AI systems.
Bio
BIO
Jeannette M. Wing is Avanessians Director of the Data Science Institute and Professor of Computer Science at Columbia University. From
2013 to 2017, she was a Corporate Vice President of Microsoft Research. She is Adjunct Professor of Computer Science at Carnegie Mellon
where she twice served as the Head of the Computer Science Department and had been on the faculty since 1985. From 2007-2010 she
was the Assistant Director of the Computer and Information Science and Engineering Directorate at the National Science Foundation. She
received her S.B., S.M., and Ph.D. degrees in Computer Science, all from the Massachusetts Institute of Technology. Professor Wing’s general
research interests are in the areas of trustworthy computing, specification and verification, concurrent and distributed systems, programming
languages, and software engineering. Her current interests are in the foundations of security and privacy, with a new focus on trustworthy AI.
She was or is on the editorial board of twelve journals, including the Journal of the ACM and Communications of the ACM. Professor Wing is
known for her work on linearizability, behavioral subtyping, attack graphs, and privacy-compliance checkers. Her 2006 seminal essay, titled
Computational Thinking, is credited with helping to establish the centrality of computer science to problem-solving in fields where previously
it had not been embraced. She is currently a member of: the National Library of Medicine Blue Ribbon Panel; the Science, Engineering, and
Technology Advisory Committee for the American Academy for Arts and Sciences; the Board of Trustees for the Institute of Pure and Applied
Mathematics; the Advisory Board for the Association for Women in Mathematics; and the Alibaba DAMO Technical Advisory Board. She
has been chair and/or a member of many other academic, government, and industry advisory boards. She received the CRA Distinguished
Service Award in 2011 and the ACM Distinguished Service Award in 2014. She is a Fellow of the American Academy of Arts and Sciences,
American Association for the Advancement of Science, the Association for Computing Machinery (ACM), and the Institute of Electrical and
Electronic Engineers (IEEE).
Hosts
IAA and CS
Video
Watch seminar video.