Led by an advisory bench of the field’s best minds

A research associate professor and co-director of the Mason Autonomy & Robotics Center whose work centers on the ethics, policy, and governance of autonomous and emerging technologies. He holds a PhD from the University of Maryland, was a research fellow at the U.S. Naval Academy and an Army Futures Command “Mad Scientist,” and has advised DARPA, RAND, and the U.S. State Department on responsible AI while helping stand up responsible-innovation and AI-ethics functions in industry. His scholarship anchors the framework’s hardest problem: governing agentic AI with action allowlists, human-approval gates, and defensible accountability for autonomous decisions.

Associate professor of AI and innovation policy at George Mason, jointly appointed across the Schar School of Policy and Government and the School of Computing, and co-director of Mason’s Center for Human-AI Innovation for Society (CHAIS). She holds a PhD in science, technology, and innovation policy from Georgia Tech. Her research measures bias in generative language models, with peer-reviewed work in Nature Communications and at the ACM FAccT and AIES conferences, and she originated the Wells-Du Bois Protocol for machine-learning bias and the framework of “emancipatory data science.” On this engagement she anchors how fairness is actually measured, intersectional and past a 4/5ths checkbox, and the equity of the OIG’s AI-workforce upskilling.

Directs Brookings’ Center for Technology Innovation and founded its AI Equity Lab; a leading national authority on algorithmic bias and on who bears the burden of automated decisions. She authored Brookings’ seminal AI-bias white paper (150,000+ downloads), the Oxford Handbook of AI Governance chapter on mitigating algorithmic bias, and the 2024 book Digitally Invisible, and has testified to Congress on responsible AI in financial services and government oversight.

Applied AI ethicists who have served on federal Responsible-AI review boards, the lens for governing autonomous, agentic systems.

Scholars who shape how institutions actually govern advanced and increasingly agentic AI.

National leaders on algorithmic bias and on who bears the burden of automated decisions.

A leading HBCU voice on equity, justice, and inclusive design in public-sector AI systems.

Researchers on robustness and fairness, holding models steady against drift and adversarial behavior.

Practitioner-scholars who operationalize the NIST AI Risk Management Framework and track real-world AI incidents.

A responsible-AI firm whose principals contribute to the NIST AI Risk Management Framework, sit on the AI Incident Database board, and authored the standard guide to machine learning for high-risk applications.