Osonde A. Osoba

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Codirector, Center for Scalable Computing and Analysis; Senior Information Scientist; Professor, Pardee RAND Graduate School
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Ph.D. & M.S. in electrical engineering, University of Southern California; B.S. in electrical and computer engineering, University of Rochester

Media Resources

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To arrange an interview, contact the RAND Office of Media Relations at (310) 451-6913, or email media@rand.org.

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Osonde Osoba (oh-shOwn-day aw-shAw-bah) is a 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 & the examination of implications of data-driven decision systems. Recurring themes in his work include algorithmic equity, modeling for decision support, & modeling behaviors.

Osoba is the associate director for Tech & Narrative Lab at the Pardee RAND Graduate School. And he codirects RAND's Center for Scalable Computing and Analysis.

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.

Selected Publications

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

Recent Media Appearances

Interviews: KPBS-TV Online; Newslaundry


  • Profile with fingerprint on a red background, photo by malerapaso/Getty Images

    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

  • A TV reporter wearing a mask, photo by brightstars/Getty Images

    Don't Make the Pandemic Worse with Poor Data Analysis

    The need for immediate answers in the face of severe public health and economic distress may create a temptation to relax statistical standards. But urgency should not preclude expert analysis and honest assessments of uncertainty. Mistaken assumptions could lead to counterproductive actions.

    May 6, 2020 The RAND Blog

  • Jennifer Bailey, VP of Apple Pay at Apple, speaks about the Apple Card during an Apple special event in Cupertino, California, March 25, 2019, photo by Stephen Lam/Reuters

    Did No One Audit the Apple Card Algorithm?

    Complex, opaque technologies like artificial intelligence provide significant benefits to society. But those benefits don't eliminate the need for accountability and transparency.

    Nov 21, 2019 The RAND Blog

  • Face detection and recognition

    Keeping Artificial Intelligence Accountable to Humans

    Artificial intelligence (AI) systems are often only as intelligent and fair as the data used to train them. To enable AI that frees humans from bias instead of reinforcing it, experts and regulators must think more deeply not only about what AI can do, but what it should do—and then teach it how.

    Aug 20, 2018 TechCrunch

  • Osonde Osoba in a RAND panel discussion in Pittsburgh, Pennsylvania, February 20, 2018

    The Human Side of Artificial Intelligence: Q&A with Osonde Osoba

    Osonde Osoba has been exploring AI since age 15. He says it's less about the intelligence and more about being able to capture how humans think. He is developing AI to improve planning and is also studying fairness in algorithmic decisionmaking in insurance pricing and criminal justice.

    May 1, 2018

  • Digital silhouettes of people

    Rethinking Data Privacy

    Society benefits from the exchange of large-scale data in many ways. Anonymization is the usual mechanism for addressing the privacy of data subjects. Unfortunately, anonymization is broken.

    Oct 5, 2016 Inside Sources