Panel 1 Speakers

Kevin Bowyer’s research interests touch on many aspects of computer vision and pattern recognition, including biometrics, data mining, object recognition, and medical image analysis. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a Fellow of the IAPR, a Golden Core member of the IEEE Computer Society, and a recipient of an IEEE Technical Achievement Award. He previously served as the EIC of the IEEE Transactions on Pattern Analysis and Machine Intelligence, and the IEEE Biometrics Compendium, and as General Chair or Program Chair of a number of major conferences. He is the General Chair of the 2017 IEEE International Conference on Automatic Face and a Chair of the 2017 CVPR Media Forensics Workshop. One specific area of focus is face recognition, a technology that has encountered controversy in recent years because of claims of bias, but that is an area Bowyer is working to improve.

Ronald Metoyer (Moderator) is an associate professor of computer science and engineering at the University of Notre Dame. He received his Ph.D. from the Georgia Institute of Technology where he worked in the Graphics, Visualization and Usability Center with a focus on modeling and visualizing the motion of pedestrians in urban and architectural scenes. In 2001, he joined the faculty in the School of Electrical Engineering and Computer Science at Oregon State University and in 2002 he received an NSF CAREER Award for his work in “Understanding the Complexities of Animated Content.” After 14 years at Oregon State University, Dr. Metoyer joined the University of Notre Dame as an associate professor and associate dean in the College of Engineering. Dr. Metoyer’s research is focused on human-computer interaction and information visualization where he seeks to support end users in dealing with the large amount of data becoming available from a growing number of sources.

Solon Barocas is a Principal Researcher in the New York City lab of Microsoft Research and an Assistant Professor in the Department of Information Science at Cornell University. He is also a Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard University. His research explores ethical and policy issues in artificial intelligence, particularly fairness in machine learning, methods for bringing accountability to automated decision-making, and the privacy implications of inference. Solon co-founded the annual workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) and later established the ACM conference on Fairness, Accountability, and Transparency (FAccT). He was previously a Postdoctoral Researcher at Microsoft Research and Princeton University and he completed his doctorate at New York University.

Sara R. Jordan is Policy Counsel, Artificial Intelligence and Ethics at the Future of Privacy Forum. Her profile includes privacy implications of data sharing, data and AI review boards, privacy analysis of AI and Machine Learning (AI/ML) technologies, and analysis of the ethics challenges of AI/ ML. Sara is an active member of the IEEE Global Initiative on Ethics for Autonomous and Intelligent Systems. Prior to working at FPF, Sara was faculty in the Center for Public Administration and Policy at Virginia Tech (2014-2020) and in the Department of Politics and Public Administration at the University of Hong Kong (2007- 2013). She is a graduate of Texas A&M University and the University of South Florida.