Face Recognition and Regulation: Q & A with Professor Erik Learned-Miller
Join professor and national expert Erik Learned-Miller to explore face recognition research and discuss a proposed solution for regulating a young technology that is prone to errors and misuse.
A troubling example of such problems was recently reported by The New York Times. In January, an African-American man named Robert Williams was arrested in Detroit for committing a crime he had nothing to do with because he was incorrectly identified by a face recognition algorithm. The police used the faulty match as conclusive evidence despite being explicitly instructed not to rely on automatic face recognition alone. This is exactly the nightmarish scenario that many concerned citizens and citizen advocacy groups have been worried about.
How did we get here? What should be done about it? Should we ban all face recognition technology? Can we find a way to benefit from the proper uses of face recognition technology while protecting against these abuses and errors?
This event is free and open to the public. Registration is required to obtain a link to the Zoom webinar.
Read more about the research:
In a new white paper, "Facial Recognition Technologies in the Wild: A Call for a Federal Office," Professor Erik Learned-Miller and co-authors argue that the recent spate of city and statewide restrictions and proposed federal legislation are not enough to address the risks posed by facial recognition technologies. Instead, the authors take a broader approach, calling for a new, FDA-inspired model for managing facial recognition technologies at the federal level.
Read more about the speaker:
Erik Learned-Miller is a professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst, where he joined the faculty in 2004. His research interests include face recognition, unsupervised learning, and learning from small training sets, vision for robotics, and motion understanding. Learned-Miller received a BA in psychology from Yale University in 1988. In 1989, he co-founded CORITechs, Inc., where he co-developed the second FDA cleared system for image-guided neurosurgery. He worked for Nomos Corporation in Pittsburgh, PA for two years as the manager of neurosurgical product engineering. He obtained MS (1997) and PhD (2002) degrees from the Massachusetts Institute of Technology, both in electrical engineering and computer science. He received an NSF CAREER Award in 2006 and, in 2019, received the PAMI Mark Everingham Award for his work on face recognition benchmarks. He was also a co-program chair for CVPR 2015 in Boston.
This event is sponsored by the UMass Amherst Center for Data Science.