Bans on Facial Recognition Are Naive. Hold Law Enforcement Accountable for Its Abuse
Broader police reform may be difficult to achieve. But in the long run, it will be more effective than any specific technology ban.
Jun 17, 2020
The Hill
Osonde Osoba (oh-shOwn-day aw-shAw-bah) is an adjunct senior information scientist at the RAND Corporation and a professor at the Pardee RAND Graduate School. Osoba's research work weaves together two strands: the principled application of artificial intelligence/machine learning (AI/ML) to diverse facets of policy research and the examination of implications of data-driven decision systems. Recurring themes in his work include algorithmic equity, modeling for decision support, and modeling behaviors.
Before RAND, Osoba was a researcher at the Signal and Image Processing Institute (SIPI) at the University of Southern California (USC) where he worked on theoretical and applied methods for speeding up machine learning algorithms. His work there is the basis of several machine-learning patents. He received his Ph.D. in electrical engineering from the University of Southern California and his B.S. in electrical and computer engineering from the University of Rochester.
Osonde A. Osoba, Benjamin Boudreaux, Douglas Yeung, "Steps Towards Value-Aligned Systems," Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 2020
Osonde A. Osoba, Bart Kosko, "Fuzzy Cognitive Maps of Public Support for Insurgency and Terrorism," Journal of Defense Modeling and Simulation, 14(1), 2017
K. Audhkhasi, O. Osoba, and B. Kosko, "Noise-Enhanced Convolutional Neural Networks," Neural Networks, 78, 2016
Osonde Osoba, Sanya Mitaim, and Bart Kosko., "The noisy expectation–maximization algorithm," Fluctuation and Noise Letters, 12(03), 2013
Interviews: KPBS-TV Online; Newslaundry