Deborah Raji

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Inioluwa Deborah Raji
Deb Raji.jpg
Alma materUniversity of Toronto
Known forAlgorithmic bias
Fairness (machine learning)
Algorithmic auditing and evaluation
Scientific career
FieldsComputer Science
InstitutionsMozilla Foundation
Partnership on AI
AI Now Institute
MIT Media Lab

Inioluwa Deborah Raji is a Nigerian-Canadian computer scientist and activist who works on algorithmic bias, AI accountability, and algorithmic auditing. Raji has previously worked with Joy Buolamwini, Timnit Gebru, and the Algorithmic Justice League on researching gender and racial bias in facial recognition technology.[1] She has also worked with Google’s Ethical AI team and been a research fellow at the Partnership on AI and AI Now Institute at New York University working on how to operationalize ethical considerations in machine learning engineering practice.[2] A current Mozilla fellow, she has been recognized by MIT Technology Review and Forbes as one of the world's top young innovators.[3][4]

Early life and education[edit]

Raji was born in Port Harcourt, Nigeria and moved to Mississauga, Ontario when she was four years old. Eventually her family moved to Ottawa, Canada.[3] She studied Engineering Science at the University of Toronto, graduating in 2019.[5][6] In 2015, she founded Project Include, a nonprofit providing increased student access to engineering education, mentorship, and resources in low income and immigrant communities in the Greater Toronto Area.[7]

Career and research[edit]

Raji worked with Joy Buolamwini at the MIT Media Lab and Algorithmic Justice League, where she audited commercial facial recognition technologies from Microsoft, Amazon, IBM, Face++, and Kairos.[8] They found that these technologies were significantly less accurate for darker-skinned women than for white men.[5][9] With support from other top AI researchers and increased public pressure and campaigning, their work led IBM and Amazon to agree to support facial recognition regulation and later halt the sale of their product to police for at least a year.[4][10][11][12] Raji also interned at machine learning startup Clarifai, where she worked on a computer vision model for flagging images.[13]

She participated in a research mentorship program at Google and worked with their Ethical AI team on creating model cards, a documentation framework for more transparent machine learning model reporting. She also co-led the development of internal auditing practices at Google.[13] Her contributions at Google were separately presented and published at the AAAI conference and ACM Conference on Fairness, Accountability, and Transparency.[14][15][16]

In 2019, Raji was a summer research fellow at The Partnership on AI working on setting industry machine learning transparency standards and benchmarking norms.[17][18] Raji was a Tech Fellow at the AI Now Institute worked on algorithmic and AI auditing. Currently, she is a fellow at the Mozilla Foundation researching algorithmic auditing and evaluation.[2]

Raji's work on bias in facial recognition systems has been highlighted in the 2020 documentary Coded Bias directed by Shalini Kantayya.[19]

Selected awards[edit]


  1. ^ Schwab, Katharine (2021-02-26). "'This is bigger than just Timnit': How Google tried to silence a critic and ignited a movement". Fast Company. Archived from the original on 2021-02-26.
  2. ^ a b "Mozilla Welcomes Two New Fellows in Trustworthy AI". Mozilla Foundation. 2020-10-16. Retrieved 2021-02-27.
  3. ^ a b c "Inioluwa Deborah Raji | Innovators Under 35". Retrieved 2021-02-26.
  4. ^ a b c "Inioluwa Deborah Raji - Forbes 30 Under 30". Forbes. Archived from the original on 2020-12-01. Retrieved 2021-02-27.
  5. ^ a b "This U of T Engineering student is holding companies accountable for biased AI facial technology". U of T Engineering News. 2019-02-11. Retrieved 2021-02-26.
  6. ^ "U of T Engineering alumna Inioluwa Deborah Raji named to MIT Technology Review's Top Innovators Under 35". U of T Engineering News. 2020-06-23. Retrieved 2021-02-27.
  7. ^ "Deborah Raji of Mozilla on Forbes 30 under 30, Mentorship in AI & more". RE•WORK Blog - AI & Deep Learning News. 2021-02-03. Retrieved 2021-02-27.
  8. ^ "Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial ai products" (PDF). Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society: 429–435. January 27, 2019.
  9. ^ Singer, Natasha (2019-01-25). "Amazon Is Pushing Facial Technology That a Study Says Could Be Biased (Published 2019)". The New York Times. ISSN 0362-4331. Retrieved 2021-02-27.
  10. ^ Heilweil, Rebecca (2020-06-10). "Why it matters that IBM is getting out of the facial recognition business". Vox. Retrieved 2021-02-27.
  11. ^ "IBM walked away from facial recognition. What about Amazon and Microsoft?". VentureBeat. 2020-06-10. Retrieved 2021-02-27.
  12. ^ "The two-year fight to stop Amazon from selling face recognition to the police". MIT Technology Review. Retrieved 2021-02-27.
  13. ^ a b "Inioluwa Deborah Raji". MIT Technology Review. Retrieved 2021-02-27.
  14. ^ Raji, Inioluwa Deborah; Gebru, Timnit; Mitchell, Margaret; Buolamwini, Joy; Lee, Joonseok; Denton, Emily (2020-01-03). "Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing". arXiv:2001.00964 [cs.CY].
  15. ^ Raji, Inioluwa Deborah; Smart, Andrew; White, Rebecca N.; Mitchell, Margaret; Gebru, Timnit; Hutchinson, Ben; Smith-Loud, Jamila; Theron, Daniel; Barnes, Parker (2020-01-03). "Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing". arXiv:2001.00973 [cs.CY].
  16. ^ Mitchell, Margaret; Wu, Simone; Zaldivar, Andrew; Barnes, Parker; Vasserman, Lucy; Hutchinson, Ben; Spitzer, Elena; Raji, Inioluwa Deborah; Gebru, Timnit (2019-01-29). "Model Cards for Model Reporting". Proceedings of the Conference on Fairness, Accountability, and Transparency: 220–229. arXiv:1810.03993. doi:10.1145/3287560.3287596. ISBN 9781450361255. S2CID 52946140.
  17. ^ Xiang, Alice; Raji, Inioluwa Deborah (2019-11-25). "On the Legal Compatibility of Fairness Definitions". arXiv:1912.00761 [cs.CY].
  18. ^ "About Face: A Survey of Facial Recognition Evaluation". DeepAI. 2021-02-01. Retrieved 2021-02-26.
  19. ^ "Coded Bias: Director Shalini Kantayya on Solving Facial Recognition's Serious Flaws". Stanford HAI. Retrieved 2021-03-15.
  20. ^ "AI innovation winners announced in San Francisco". Innovation Matrix. 2019-07-12. Archived from the original on 2020-12-09. Retrieved 2021-02-27.
  21. ^ "Pioneer Award Ceremony 2020". Electronic Frontier Foundation. 2020-08-24. Retrieved 2021-02-27.
  22. ^ "Hall of Fame". 100 Brilliant Women in AI Ethics™. Retrieved 2021-02-27.