AI has enormous potential to improve data-driven decision making in the public sector. However, numerous case studies demonstrate that AI can perpetuate human biases -- contrary to commonly held perceptions that computer outputs are strictly objective. As the public sector increasingly uses AI for government operations and services, what can government leaders do to ensure equitable development and deployment of AI? Could AI tools be used to mitigate human bias? How can local and state governments benefit from AI services while avoiding risks for AI bias? Experts will discuss sources of AI bias and the tools for effectively and equitably integrating AI systems into public operations and services.

Speakers

Gabriella Waters, Director of Operations, Director - Cognitive and Neurodiversity AI Lab, Center for Equitable AI & Machine Learning Systems, Morgan State University

Gabriella Waters is an artificial intelligence and machine learning researcher and the Director of Operations at the Center for Equitable AI & Machine Learning Systems at Morgan State University in Baltimore, MD. She is the director of the Cognitive & Neurodiversity AI (CoNA) Lab, a professor at the Propel Center, where she facilitates the Culturally Relevant AI/ML Systems course, and a research associate at NIST where she leads AI testing and evaluation across three teams. She is passionate about increasing the diversity of thought around technology and focuses on interdisciplinary collaborations to drive innovation, equity, explainability, transparency, and ethics in the development and application of AI tools. In her research, Gabriella is interested in studying the intersections between human neurobiology & learning, quantifying ethics & equity in AI/ML systems, neuro-symbolic architectures, and intelligent systems that make use of those foundations for improved human-computer synergy. She develops technology innovations with an emphasis on support for neurodiverse populations.

Suresh Venkatasubramanian, Director, Center for Tech Responsibility, Brown University

Suresh Venkatasubramanian is a Professor of Data Science, Computer Science, and the Humanities at Brown University. Suresh's background is in algorithms and computational geometry, as well as data mining and machine learning. His current research interests lie in algorithmic fairness, and more generally the impact of automated decision-making systems in society. Suresh recently finished a stint in the Biden-Harris administration, where he served as Assistant Director for Science and Justice in the White House Office of Science and Technology Policy. In that capacity, he helped co-author the Blueprint for an AI Bill of Rights.

Prior to Brown University, Suresh was at the University of Utah, where as an assistant professor he was the John and Marva Warnock Assistant Professor. He has received a CAREER award from the NSF for his work in the geometry of probability, a test-of-time award at ICDE 2017 for his work in privacy, and a KAIS Journal award for his work on auditing black-box models. His research on algorithmic fairness has received press coverage across the globe, including NPR's Science Friday, NBC, and CNN, as well as in other media outlets. He is a past member of the Computing Community Consortium Council of the CRA, spent 4 years (2017-2021) as a member of the board of the ACLU in Utah, and is a past member of New York City's Failure to Appear Tool (FTA) Research Advisory Council, the Research Advisory Council for the First Judicial District of Pennsylvania and the Utah State Auditor's Commission on protecting privacy and preventing discrimination. He was recently named by Fast Company to their AI20 list of thinkers shaping the world of generative AI.