Recent years have seen astounding growth in the 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 life-critical 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.
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About the speaker: Dr. Wing joined Columbia in 2017 as the inaugural Avanessians Director of the Data Science Institute. Prior to Columbia, Dr. Wing was Corporate Vice President of Microsoft Research, served on the faculty and as department head in computer science at Carnegie Mellon University, and served as Assistant Director for Computer and Information Science and Engineering at the National Science Foundation. Dr. Wing’s research contributions have been in the areas of trustworthy AI, security and privacy, specification and verification, concurrent and distributed systems, programming languages, and software engineering. 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 and thereby influencing K-12 and university curricula worldwide.

She is a Fellow of the American Academy of Arts and Sciences, the American Association for the Advancement of Science, the Association for Computing Machinery (ACM), and the Institute of Electrical and Electronic Engineers (IEEE). Dr. Wing received distinguished service awards from the Association for Computing Machinery and the Computing Research Association and an honorary doctorate degree from Linköping University, Sweden. She earned her bachelor’s, master’s, and doctoral degrees in computer science, all from MIT.