Timnit Gebru

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Timnit Gebru
T Gebru Visual Computational Sociology (cropped).jpg
Timnit Gebru
Alma materStanford University
Known forAlgorithmic bias
Scientific career
InstitutionsMicrosoft Research

Timnit Gebru is an Eritrean computer scientist and the technical co-lead of the Ethical Artificial Intelligence Team at Google. She works on algorithmic bias and data mining. She is an advocate for diversity in technology and is the cofounder of Black in AI, a community of black researchers working in artificial intelligence.

Early life and education[edit]

Gebru is an Eritrean origin born and raised in Ethiopia.[1] Her father and two oldest sisters are electrical engineers.[2] Her father died when she was five years old and she was raised by her mother.[3] She escaped potential forced deportation by Ethiopian government to Eritrea in the late 1990's and traveled to Ireland. She then immigrated to the United States to join her mother and siblings. She was accepted to study at Stanford University.[1] There she earned her Bachelor's and Master's degrees in electrical engineering.[4] Gebru worked at Apple Inc., developing signal processing algorithms for the first IPad.[5] Gebru earned her doctorate under the supervision of Fei-Fei Li at Stanford University in 2017. She used data mining of publicly available images.[6] She was interested in the amount of money spent by governmental and non-governmental organisations trying to collect information about communities.[7] To investigate alternatives, Gebru combined deep learning with Google Street View to estimate the demographics of United States neighbourhoods, showing that socioeconomic attributes such as voting patterns, income, race and education can be inferred from observations of cars.[4] If the number of pickup trucks outnumbers the number of sedans, the community are more likely to vote for the Republican party.[8] They analysed over 15 million images from the 200 most populated US cities.[9] The work was extensively covered in the media, being picked up by BBC News, Newsweek, The Economist and The New York Times.[10][11][12]

Gebru presented her research at the 2017 LDV Capital Vision Summit competition, where computer vision scientists present their work to members of industry and venture capitalists.[13] Gebru won the competition, starting a series of collaborations with other entrepreneurs and investors.[13] Both during her PhD in 2016 and in 2018, Gebru returned to Ethiopia with Jelani Nelson's programming campaign AddisCoder.[14][15] After her PhD, Gebru joined Microsoft as a postdoctoral researcher in the Fairness, Accountability, Transparency and Ethics in AI (FARE) lab.[9][16] She was honoured by the Selfpreneur as "the Alicorn in Artificial Intelligence".[9]

Career and research[edit]

Gebru revealing that you can predict, with some reliability, the way an American will vote from the type of vehicle they drive.

Gebru works at Google on the ethics of Artificial Intelligence. She studies the implications of artificial intelligence, looking to improve the ability of technology to do social good.[17] She collaborated with the MIT research group Gender Shades.[18] Gebru worked with Joy Buolamwini to investigate facial recognition software; finding that black women were 35% less likely to be recognised than white men.[19] When Gebru attended an artificial intelligence conference in 2016, she noticed that she was the only black woman out of 8,500 delegates.[20] Together with her colleague Rediet Abebe Gebru founded Black in AI,[21] a community of black researchers working in artificial intelligence. Black in AI have held workshops at the Conference on Neural Information Processing Systems annually since 2017.[22] She has discussed bias in artificial intelligence in podcasts and interviews.[23][24]

Gebru also worked on Microsoft's Fairness, Accountability, Transparency, and Ethics in the AI team. In 2017, Gebru spoke on the Fairness and Transparency conference, where MIT Technology Review interviewed her about the biases that exist in AI systems and how adding diversity in AI teams can fix that issue. In her interview with Jackie Snow, Snow asked Gebru the question of "How does the lack of diversity distort artificial intelligence and specifically computer vision?" and Gebru responds by pointing out that there are biases that exist in the software developers. Without diversity in the software developers, we will not be able to address the issues of algorithmic biases that people face in the world.


Gebru, who is one of the most prominent researchers in artificial intelligence, began questioning Amazon's facial recognition technology because of its biases against women of color. Gebru and other artificial intelligence researchers signed a letter that reflected the systematic issues that reside in Amazon's facial recognition software. A study that was conducted by MIT researchers shows that Amazon's facial recognition system had trouble identifying darker-skinned females than any other technology companies facial recognition software.[26]


In 2017 Gebru was featured on the Forbes list of incredible women advancing AI research.[27]


  1. ^ a b Lahde, Lisa. "AI Innovators: How One Woman Followed Her Passion and Brought Diversity to AI". Forbes. Retrieved 2019-01-09.
  2. ^ "Final | Timnit Gebru". Campaign | 1 million women in STEM. Retrieved 2019-01-09.
  3. ^ Chisling, Ava (2017-07-24). "Excuse me, sir, but where are all the women?". ROSS Intelligence. Retrieved 2019-01-09.
  4. ^ a b AI, People In (2017-09-16). "Timnit Gebru honored as an Alicorn of Artificial Intelligence by People in AI". Selfpreneur. Retrieved 2019-01-09.
  5. ^ "Timnit Gebru". Databricks. Retrieved 2019-01-09.
  6. ^ "Understanding the Limits of AI: When Algorithms Fail". MIT Tech Review. Retrieved 2019-01-09.
  7. ^ Capital, L. D. V. (2017-08-01), Timnit Gebru - 2017 Entrepreneurial Computer Vision Challenge Finalist Presentations, retrieved 2019-01-09
  8. ^ Fei-Fei, Li; Aiden, Erez Lieberman; Deng, Jia; Chen, Duyun; Wang, Yilun; Krause, Jonathan; Gebru, Timnit (2017-12-12). "Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States". Proceedings of the National Academy of Sciences. 114 (50): 13108–13113. doi:10.1073/pnas.1700035114. ISSN 1091-6490. PMC 5740675. PMID 29183967.
  9. ^ a b c "Timnit Gebru". Design Better. Retrieved 2019-01-09.
  10. ^ Lufkin, Bryan. "What Google Street View tells us about income". www.bbc.com. Retrieved 2019-01-09.
  11. ^ "A machine-learning census of America's cities". The Economist. 2017-03-02. ISSN 0013-0613. Retrieved 2019-01-09.
  12. ^ Lohr, Steve (2017-12-31). "How Do You Vote? 50 Million Google Images Give a Clue". The New York Times. ISSN 0362-4331. Retrieved 2019-01-09.
  13. ^ a b "Timnit Gebru Wins 2017 ECVC: Leveraging Computer Vision to Predict Race, Education and Income via Google Streetview Images". LDV Capital. Retrieved 2019-01-09.
  14. ^ Magazine, Tadias. "Timnit Gebru: Among Incredible Women Advancing A.I. Research at Tadias Magazine". Retrieved 2019-01-09.
  15. ^ "History | AddisCoder". www.addiscoder.com. Retrieved 2019-01-10.
  16. ^ "Timnit Gebru". World Science Festival. Retrieved 2019-01-09.
  17. ^ University, Office of Web Communications, Cornell. "Digital Life Seminar | Timnit Gebru". Cornell. Retrieved 2019-01-09.
  18. ^ "Team". MIT Media Lab. Retrieved 2019-01-09.
  19. ^ Lohr, Steve (2018-02-09). "Facial Recognition Is Accurate, if You're a White Guy". The New York Times. ISSN 0362-4331. Retrieved 2019-01-09.
  20. ^ Birhaner, De (2017-05-26). "Ethiopian Ms. Timnit Gebru Fights Algorithmic Bias And Homogenous Thinking in A.I." De Birhan. Retrieved 2019-01-09.
  21. ^ Black in AI
  22. ^ AI, Black in. "Workshops". Black in AI. Retrieved 2019-01-09.
  23. ^ "Facial Recognition, Demographic Analysis and More with Timnit Gebru". Georgian Partners. 2018-12-20. Retrieved 2019-01-09.
  24. ^ "3 Ways to Counter Unconscious Bias in AI". Salesforce Blog. Retrieved 2019-01-09.
  25. ^ Mitchell, Margaret; Wu, Simone; Zaldivar, Andrew; Barnes, Parker; Vasserman, Lucy; Hutchinson, Ben; Spitzer, Elena; Raji, Inioluwa Deborah; Gebru, Timnit (2019). "Model Cards for Model Reporting". Proceedings of the Conference on Fairness, Accountability, and Transparency - FAT* '19. New York, New York, USA: ACM Press. doi:10.1145/3287560.3287596. ISBN 978-1-4503-6125-5.
  26. ^ Mitchell, Andrea (April 2019). "A.I. Experts Question Amazons Facial-Recognition Technology". ICT Monitor Worldwide; Amman.
  27. ^ Yao, Mariya. "Meet These Incredible Women Advancing A.I. Research". Forbes. Retrieved 2019-11-21.

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