Inioluwa Deborah Raji
Algorithmic Auditing Fellow at Mozilla & CS PhD Candidate at UC Berkeley
Inioluwa Deborah Raji is a fellow at the Mozilla Foundation, CS PhD candidate at UC Berkeley, and Mozilla Tech Fellow. Deborah is widely recognized for her seminal research on algorithmic bias and commercial auditing, including co-authoring the landmark Gender Shades evaluation with Joy Buolamwini that exposed demographic disparities in facial analysis software and led to industry-wide policy changes.
Areas of focus
Professional niches
Proof of Work
Outsider Oversight: Designing Independent External Audits for Deployed AI
An influential empirical paper by Deborah Raji examining the methodology and systemic impact of third-party external audits on proprietary machine learning systems deployed in criminal justice and hiring.
External audit methodology; access to internal training datasets is often obstructed by commercial trade secrecy.
Context: Evaluates algorithmic accountability across automated facial recognition and predictive risk scoring algorithms.
View missionMozilla Foundation / UC Berkeley: Enterprise AI Risk Management & Governance Framework
A structured governance and compliance methodology authored by Inioluwa Deborah Raji at Mozilla Foundation / UC Berkeley, establishing organizational guardrails, vendor evaluation criteria, and model validation standards for enterprise adoption.
Strategic governance advisory framework; regulatory compliance requirements must be validated against regional legal statutes.
Context: Computer vision benchmarks, statistical auditing methodologies, and demographic parity metrics.
View mission