Deborah Raji

Early Life and Education in Canada

Deborah Raji was born in Port Harcourt, Nigeria, and moved to Mississauga, Ontario, with her family at the age of four, later relocating to Ottawa. She attended Colonel By Secondary School, where she completed the rigorous International Baccalaureate programme. Raji pursued higher education at the University of Toronto, enrolling in the Engineering Science program, a broad and intensive curriculum known for its academic demands. She graduated with a Bachelor of Applied Science in 2019, laying a technical foundation that would later inform her critical approach to machine learning systems. During her undergraduate studies in 2015, Raji founded Project Include, a nonprofit organization aimed at increasing access to engineering education, mentorship, and resources for students from low income and immigrant communities in the Greater Toronto Area. This early initiative demonstrated her commitment to equity in technology, a theme that would define her career. In August 2021, she began her doctoral studies in computer science at the University of California, Berkeley, where she continues her research on algorithmic auditing and evaluation.

Pioneering Work on Facial Recognition Bias

Raji's foundational work in algorithmic bias began in earnest during the summer of 2017 following her graduation, when she took a position at the computer vision company Clarifai. While working on a dataset for a facial recognition model, she immediately noticed a critical lack of representation, particularly the absence of individuals with darker skin tones, especially women. This personal experience of not seeing herself reflected in the data prompted her to reach out to researcher Joy Buolamwini at the MIT Media Lab. Raji subsequently collaborated with Buolamwini, Timnit Gebru, and the Algorithmic Justice League on landmark audits of commercial facial recognition technologies. Their research, which audited systems from major companies including Microsoft, Amazon, IBM, Face++, and Kairos, revealed severe performance disparities. The technologies were found to be significantly less accurate for darker-skinned women compared to white men, with error rates sometimes being orders of magnitude higher. The 2018 audit specifically found these systems did not work at all on the darker-skinned faces that had been excluded from the training datasets. This technical deficiency translated into real-world harms, such as the wrongful arrest of individuals like Robert Williams due to incorrect facial recognition matches.

The publication of these findings, combined with sustained public advocacy and pressure from the research community, led to substantial industry and policy shifts. IBM and Amazon publicly announced their support for federal regulation of facial recognition technology. Both companies, along with others, temporarily halted the sale of these systems to law enforcement agencies for at least a year. This work was prominently featured in the 2020 documentary Coded Bias, directed by Shalini Kantayya, bringing the issue of algorithmic discrimination to a wider public audience. Raji's contributions in this area were recognized with the 2019 VentureBeat AI Innovations Award for AI for Good and the 2020 Electronic Frontier Foundation Pioneer Award, which she shared with Buolamwini and Gebru. Her work on this issue continued to be highlighted in later media, including her participation in the 2026 documentary The AI Doc: Or How I Became an Apocaloptimist directed by Daniel Roher.

Research and Advocacy for AI Accountability

Building on her audit work, Raji has focused on developing practical frameworks and norms to embed accountability into the machine learning development lifecycle. During a research mentorship at Google, she worked with the company's Ethical AI team to create and promote 'model cards,' a documentation framework designed to standardize and improve transparency in machine learning model reporting. She also co-led the development of internal auditing practices at Google, with her contributions presented at major conferences like AAAI and the ACM Conference on Fairness, Accountability, and Transparency. In 2019, Raji was a summer research fellow at The Partnership on AI, working on setting industry machine learning transparency standards and benchmarking norms. She also served as a Tech Fellow at the AI Now Institute at New York University, working specifically on algorithmic and AI auditing methodologies. These fellowships positioned her at the intersection of academic research and industry practice, allowing her to push for operational changes in how companies evaluate their systems.

Her current research as a former Mozilla Senior Trustworthy AI Fellow and PhD candidate interrogates the limitations of standard AI benchmarking and evaluation practices. Raji argues that real-world deployment of machine learning systems in high-stakes domains like law, medicine, and content moderation reveals failure modes that are not captured by laboratory tests. She has worked on projects, supported by the Mozilla Foundation and the MacArthur Foundation, that audit system failures in these consequential contexts. Her advocacy now extends to policy, where she addresses challenges faced by auditors, including corporate retaliation. Raji advocates for legal protections for third-party investigators, including audit access and data protections, to ensure independent scrutiny of automated systems. She serves on the advisory boards of several organizations dedicated to AI governance, including the Center for Democracy and Technology AI Governance Lab, the Health AI Partnership, TeachAI, REALML, and the Center for Civil Rights and Technology.

Honours and Recognition

Deborah Raji has received widespread acclaim for her research and advocacy, establishing her as one of the most influential young voices in the field of ethical AI. Her awards highlight both the technical and societal impact of her work. In 2020, just one year after graduating from the University of Toronto, she was named to the MIT Technology Review's prestigious 35 Under 35 list as an Innovator. The following year, she was included in the Forbes 30 Under 30 list for Enterprise Technology. In 2023, Time magazine recognized her as one of the 100 Most Influential People in AI. Beyond these list-based honours, Raji has received specific awards for her contributions to the public interest. Her writing has also reached broad audiences through publications like The Atlantic, where she authored an article titled 'AI's Present Matters More Than Its Imagined Future.' Her undergraduate research was featured in a New York Times story in 2019, and she is a frequent keynote speaker, such as for the CARE-AI Seminar Series in November 2026. These platforms allow her to critique existing machine learning development norms and discuss evolving practices to address legal challenges and operational failures, especially for marginalized populations.

• 2019: VentureBeat AI Innovations Award in AI for Good, shared with Joy Buolamwini and Timnit Gebru.
• 2020: Electronic Frontier Foundation Pioneer Award, shared with Buolamwini and Gebru.
• 2021: Honouree, 100 Brilliant Women in AI Ethics Hall of Fame.
• 2024: Recipient of the Tech For Humanity Prize, awarded at the 2024 Tech for Humanity Summit.
• 2024: Recipient of the Mozilla Rise 25 award.

Current Roles and Ongoing Work

As of 2026, Deborah Raji balances multiple roles that bridge academia, civil society, and policy. She is actively completing her PhD in computer science at the University of California, Berkeley, where her dissertation research continues to focus on algorithmic auditing and evaluation. Her PhD work specifically examines how model engineering choices, from evaluation to data selection, impact model behaviour in deployment, and how different stakeholders interpret performance measurements. Concurrently, she holds an Academic Fellowship with the Leadership Conference on Civil and Human Rights, applying her technical expertise to policy advocacy for civil rights in the age of automation. Raji was formerly a Senior Trustworthy AI Fellow at the Mozilla Foundation, a role that supported her independent research into the failure of AI systems in high-stakes contexts. Her advisory work spans numerous organizations focused on the responsible development and deployment of AI. Raji's current projects investigate the legal and institutional frameworks necessary for accountability, including consumer protection, product liability, procurement, and anti-discrimination practice as it applies to machine learning systems. She draws parallels to historical consumer safety advocacy, citing Ralph Nader's work on automobile regulation and stories of individuals harmed by algorithmic failures like Carmelita Colvin and Brian Johnson, who were falsely accused of unemployment fraud. Raji's ongoing public engagement includes participating in the 2026 documentary The AI Doc: Or How I Became an Apocaloptimist, extending her reach as a communicator on the societal implications of artificial intelligence. Her research applies to a broad range of machine learning deployments, including automated decision systems, recommendation systems, and large pre-trained models, operating at the intersection of law and policy, applied economics, and computer science.

Public Engagement and Media Contributions

Deborah Raji has consistently engaged with the public and media to demystify the technical and ethical challenges of artificial intelligence. Her participation in the 2020 documentary Coded Bias was a significant moment that translated complex audit findings into a narrative accessible to a general audience, highlighting the human impact of biased algorithms. She continued this media engagement with her role in the 2026 documentary The AI Doc: Or How I Became an Apocaloptimist, directed by Daniel Roher, which explores perspectives on artificial intelligence's future. Raji is also an accomplished writer, having authored the article 'AI's Present Matters More Than Its Imagined Future' for The Atlantic magazine, where she argues for focusing on current, measurable harms rather than speculative future risks. She has been a featured speaker at major conferences and seminar series, including as a keynote for the University of Guelph's CARE-AI Seminar Series in late 2026 and as a participant in the 2024 Tech for Humanity Summit, which convened leaders from civil society, government, academia, and industry. In her talks and writings, Raji often shares her personal journey into the field, describing her initial naivete after her robotics degree and the moment questioning dataset representation at Clarifai. She credits mentors like Joy Buolamwini, Timnit Gebru, Margaret Mitchell, and policy mentor Alondra Nelson for guiding her development. Raji uses specific case studies to ground her arguments, such as the story of Robert Comstock, a mechanic injured by a car without brakes, to analogize the need for regulation in AI. She emphasizes that AI systems continue to fail the most vulnerable, citing documented cases of wrongful arrests and false fraud accusations driven by algorithmic errors. This public engagement is not merely informational but is aimed at building a coalition for change, encouraging shared visions for a humane and democratic technological future as called for at the Tech for Humanity Summit.

Questions about Deborah Raji

What is Deborah Raji known for?

Deborah Raji is best known for her pioneering research exposing racial and gender bias in commercial facial recognition systems. Her collaborative audits with the Algorithmic Justice League revealed that these technologies were significantly less accurate for darker-skinned individuals, especially women. This work led major tech companies like IBM and Amazon to change their practices, pause police sales, and support regulatory measures. She is also recognized for developing accountability frameworks like model cards at Google and for her ongoing advocacy for algorithmic auditing and evaluation standards across the industry.

Where did Deborah Raji go to university in Canada?

Deborah Raji completed her undergraduate degree in Canada at the University of Toronto. She graduated in 2019 with a Bachelor of Applied Science in Engineering Science, a demanding and interdisciplinary program. During her time at U of T, she founded the nonprofit Project Include in 2015 to support engineering education and mentorship in low income and immigrant communities in the Greater Toronto Area. Her research into algorithmic bias began during her undergraduate studies and was later featured in a New York Times story in 2019.

What awards has Deborah Raji won?

Raji has received numerous prestigious awards. These include the MIT Technology Review 35 Under 35 Innovator Award (2020), the Forbes 30 Under 30 in Enterprise Technology (2021), and a place on Time magazine's 100 Most Influential People in AI list (2023). She is also a co-recipient of the Electronic Frontier Foundation Pioneer Award (2020) and the VentureBeat AI for Good Award (2019), both shared with colleagues Joy Buolamwini and Timnit Gebru. More recent honours include the 2024 Tech For Humanity Prize, the 2024 Mozilla Rise 25 award, and being named to the 100 Brilliant Women in AI Ethics Hall of Fame in 2021.

What is Deborah Raji's role at Mozilla?

Deborah Raji is a fellow at the Mozilla Foundation, where she researches algorithmic auditing and evaluation. Her fellowship involves investigating the real-world failures of AI systems in high-stakes contexts. This work has been supported by grants from foundations like the Mozilla Foundation and the MacArthur Foundation. Previously, she held the title of Senior Trustworthy AI Fellow at the Mozilla Foundation.

How has Deborah Raji's work impacted the tech industry?

Raji's work has directly influenced corporate policies and the broader regulatory conversation around AI. Her audits of facial recognition technology contributed to decisions by IBM and Amazon to pause sales of these systems to police and to advocate for federal regulation. Her development of model cards at Google introduced a new standard for transparency in machine learning documentation. Her advocacy for third-party audit rights and her advisory roles with multiple AI governance labs push the entire field toward greater consideration of auditability, fairness, and accountability in AI development and deployment.

What is Deborah Raji studying for her PhD?

Deborah Raji is a PhD candidate in computer science at the University of California, Berkeley. Her doctoral research focuses on algorithmic auditing and evaluation, critically examining how machine learning models perform in real-world deployment beyond controlled benchmarks. She investigates three main areas: how engineering choices affect model behaviour in deployment, how stakeholders use performance measurements, and what legal frameworks for accountability are required. Her work operates at the intersection of law and policy, applied economics, and computer science to address functional harms from automated systems.

Sources and further reading

• Deborah Raji (Wikipedia)
• Deborah Raji, Engineering Science, University of Toronto (engsci.utoronto.ca)
• Centering Human Relationships in the Age of AI (pit-un.org)
• CARE-AI Seminar Series: Deborah Raji (uoguelph.ca)
• Inioluwa Deborah Raji (rajiinio.github.io)

Checked by the Your Space Hamilton desk. Last updated 2026-10-29.