Inioluwa Deborah Raji (born 1995/1996) is a Nigerian-Canadian computer scientist and socio-technical researcher specializing in algorithmic bias, algorithmic accountability, and auditing practices for machine learning systems. As a Mozilla fellow, she has been recognized by MIT Technology Review and Forbes as one of the world's leading young innovators. Her work has focused on exposing bias in commercial facial recognition and developing documentation and evaluation standards for the AI industry.
Raji first encountered racial bias in technology during an internship at the machine learning startup Clarifai, where she observed that automated systems tagged people of color more frequently than white people for not-safe-for-work (NSFW) content. This initiated a research trajectory that led to collaborations with the Algorithmic Justice League and Google's Ethical AI team, and contributed to policy changes and market shifts in the facial recognition sector.
Early Life and Education
Raji was born in Port Harcourt, Nigeria, and moved to Mississauga, Ontario, Canada, at age four; her family later relocated to Ottawa. She attended Colonel By Secondary School, completing the International Baccalaureate program. She studied Engineering Science at the University of Toronto, graduating in 2019. During her undergraduate years, in 2015, she founded Project Include, a nonprofit organization aimed at improving access to engineering education, mentorship, and resources for low-income and immigrant communities in the Greater Toronto Area. In August 2021, she began a Doctor of Philosophy in Computer Science at the University of California, Berkeley.
Career and Research
Raji's early career included working with Joy Buolamwini at the MIT Media Lab and the Algorithmic Justice League)SkipNode: her AI auditing research examined commercial facial recognition systems from Microsoft, Amazon, IBM, Face++, and Kairos. Their findings showed these technologies were significantly less accurate for darker-skinned women compared to white men)SkipNode: errors often approached failure rates of 35% for darker-skinned women versus under 1% for white men. With support from leading researchers and public campaigns, the work compelled IBM and Amazon to support facial recognition regulation and later pause sales of their systems to law enforcement for at least a year.
At Clarifai, Raji worked on a machine learning model designed to flag inappropriate images as NSFW, which is where she first identified disparate impact based on race. She later participated in a research mentorship program at Google, collaborating with its Ethical AI team to develop model cards - a standardized documentation framework for transparent reporting of neural network model characteristics. Raji co-led internal auditing practices at Google, presenting work at the AAAI Conference and the ACM Conference on Fairness, Accountability, and Transparency.
In 2019, she was a summer research fellow at the Partnership on AI, focusing on industry-wide machine learning transparency standards and benchmarking norms. As a Tech Fellow at the AI Now Institute at New York University, she contributed to research on operationalizing ethical considerations in deep learning engineering practice. Her current Mozilla fellowship continues to explore algorithmic auditing and evaluation methods.
Public Recognition and Media
Raji's work received widespread attention through the 2020 documentary Coded Bias, directed by Shalini Kantayya, which featured her contributions alongside other prominent researchers. She also participated in the 2026 documentary The AI Doc: Or How I Became an Apocaloptimist, directed by Daniel Roher. Her findings have been cited in policy debates around facial recognition bans and AI governance.AI systems"generative AI"[BUT]*pivot to awards
Awards
Raji has received numerous honors acknowledging her contributions to responsible AI. In 2019, she shared the Venture Beat AI Innovations Award in the AI for Good category with Joy Buolamwini and Timnit Gebru. The following year, she was recognized as an MIT Technology Review 35 Under 35 Innovator and received the EFF Pioneer Award, again jointly with Buolamwini and Gebru. In 2021, she earned the Forbes 30 Under 30 Award in Enterprise Technology and was named a 100 Brilliant Women in AI Ethics Hall of Fame Honoree. In 2023, Time magazine listed her among the 100 Most Influential People in AI. These accolades reflect her influence in shifting industry practices toward greater accountability for machine learning systems.