Inioluwa Deborah Raji

Inioluwa Deborah Raji is a Nigerian-Canadian computer scientist and a Ph.D. candidate in Computer Science at the University of California, Berkeley, where her research centers on algorithmic auditing and the legal and institutional accountability required for machine learning systems to be deployed safely. An Engineering Science graduate of the University of Toronto (2019), she has also held fellowships at the Mozilla Foundation and currently serves as an Academic Fellow at the Leadership Conference on Civil and Human Rights. Her work interrogates how model evaluation choices, performance metrics, and weak consumer-protection frameworks allow real-world AI harms to go unchecked.

Raji is best known for her empirical audits of commercial AI systems. Building on work with Joy Buolamwini and Timnit Gebru, she co-authored audits of commercial facial recognition products that exposed large accuracy disparities for darker-skinned women — research that contributed to decisions by IBM and Amazon to restrict facial recognition sales to law enforcement. She has worked with the Algorithmic Justice League and Google's Ethical AI team, and contributed to Google's "model cards" framework for transparent documentation of ML systems. Her broader agenda argues for third-party auditor access and standardized accountability mechanisms across the AI industry.

Her contributions have been widely recognized. She was named to MIT Technology Review's 35 Innovators Under 35 (2020), Forbes 30 Under 30 in Enterprise Technology (2021), and TIME's inaugural list of the 100 Most Influential People in AI (2023). She is also a co-recipient of the Electronic Frontier Foundation's Pioneer Award (2020, with Buolamwini and Gebru) and was named to the Mozilla Rise 25 list (2024).

At the camp: PM Lecturer — 1:30 PM Lecture - Bias & ethics in deploying ML4H.