Bans on Facial Recognition Are Naive. Hold Law Enforcement Accountable for Its Abuse.
I am Osonde Osoba, a researcher working on the societal implications of artificial intelligence: its fairness, governance, and policy.
I currently work on responsible AI at LinkedIn, building production systems that measure AI fairness under strong privacy constraints. My approach pairs technical machine-learning research with policy analysis.
Career trajectory
My training is in electrical engineering; my dissertation established conditions under which injected noise speeds up the training of statistical learning algorithms, work that underpins several patents.
From 2014 to 2021 I conducted policy research at the RAND Corporation. I applied AI across policy domains (public health, national security, and economic equity) and led research on the societal implications of AI, including a cross-disciplinary framework for algorithmic equity. I held research-leadership roles and taught graduate courses at the Pardee RAND Graduate School and USC.
Current interests
- Institutional AI governance: aligning AI-equipped institutions with their stated values.
- Regulatory fragmentation: how divergent rules across jurisdictions shape where AI's benefits and harms land.
- Class differences in AI adoption: uneven frictions in adoption and the divides they may signal.
Featured below are pieces on which I am first or second author, grouped by research area. The full record, including co-authored work, is on Google Scholar.
Algorithmic Fairness, AI Ethics & Privacy
AI for National Security & Defense
Computational Social Science & Policy Modeling
Fuzzy Systems & Causal Modeling
Noise Benefits in Machine Learning
Teaching
Graduate-level courses across the technical and policy sides of AI and data science:
- USC Viterbi School of Engineering: probability and statistics, machine learning, and stochastic processes.
- Pardee RAND Graduate School: data science, machine learning, and technology policy.
Archived course material: EE 503, Probability for Electrical & Computer Engineers lecture notes (USC, Fall 2013).
Appointments & professional service
- Commissioner, US Chamber of Commerce Commission on AI Competitiveness, Inclusion, and Innovation (2022)
- Judge, XPRIZE Pandemic Response Challenge (2021)
- RAND Corporation (2014–2021)
- Senior Information Scientist
- Co-director, Center for Scalable Computing and Analysis (SCAN)
- Associate Director, Tech & Narrative Lab, Pardee RAND Graduate School
- Dissertation & admissions committees, Pardee RAND Graduate School
- Peer review & program committees
- Area Chair, ACM FAccT 2021 (Data and Algorithm Evaluation track)
- Reviewer: ICML (2020), ACM FAT* (2018), IJCNN (2017)
- Session Discussant: WE ROBOT (2020)
- Member, Institute of Electrical and Electronics Engineers (IEEE)
Posts
Early Research Summary (pre-2018)
An archived summary of my foundational work: noise benefits in statistical machine learning, Bayesian function approximation, and the early turn toward ML for policy.
Commentary & op-eds
Did No One Audit the Apple Card Algorithm?
Keeping Artificial Intelligence Accountable to Humans.
Rethinking Data Privacy.
Selected speeches & talks
AI and the Equitable Society
Making AI Fair
Ethical Problems around Group Action in the Context of National Security
Data Privacy-Preservation Mechanisms: Current Theory & Limitations
AI in Governance: Pitfalls and Promises
Dengue Forecasting with Time-Delayed Neural Networks