Timnit Gebru - Stanford AI Lab

![](https://ai.stanford.edu/~tgebru/img/me_dare_cropped.jpg)

# Timnit Gebru

tgebru@gmail.com

I am currently a research scientist at Google in the ethical AI team. Prior to that I did a postdoc at Microsoft Research, New York City in the FATE (Fairness Transparency Accountability and Ethics in AI) group, where I studied algorithmic bias and the ethical implications underlying projects aiming to gain insights from data (see [this New York Times article](https://www.nytimes.com/2018/02/09/technology/facial-recognition-race-artificial-intelligence.html) for an example of my work). I received my PhD from the Stanford Artificial Intelligence Laboratory, studying computer vision under Fei-Fei Li. My thesis pertains to using large scale publicly available images to gain sociological insight, and working on computer vision problems that arise as a result. [The Economist](http://www.economist.com/news/science-and-technology/21717804-millions-images-public-streets-offer-cheap-sweeping-view-americas), [The New York Times](https://www.nytimes.com/2017/12/31/technology/google-images-voters.html) and others have covered part of this work. Prior to joining Fei-Fei's lab I worked at Apple designing circuits and signal processing algorithms for various Apple products including the first iPad. I also spent an obligatory year as an entrepreneur (as all Stanford undergrads seem to do). My research was supported by the NSF foundation GRFP fellowship and the Stanford DARE fellowship

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# Publications

![](https://ai.stanford.edu/~tgebru/img/modelcards.jpg)

### Model Cards for Model Reporting

Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, Timnit Gebru

FAT\* 2019

[PDF](https://arxiv.org/abs/1810.03993)

![](https://ai.stanford.edu/~tgebru/img/datasheet.png)

### Datasheets for Datasets

Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumeé III, Kate Crawford

FAT/ML 2018

[PDF](https://arxiv.org/abs/1803.09010)

![](https://ai.stanford.edu/~tgebru/img/gendershades.png)

### Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification

Joy Buolamwini and Timnit Gebru

FAT\* 2018

[PDF](http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf)

[Project Website](http://gendershades.org/)

![](https://ai.stanford.edu/~tgebru/img/iccv_website.jpg)

### Fine-grained Recognition in the Wild: A Multi-Task Domain Adaptation Approach

Timnit Gebru, Judy Hoffman, Li Fei-Fei

ICCV 2017

[PDF](https://ai.stanford.edu/~tgebru/papers/iccv.pdf)

![](https://ai.stanford.edu/~tgebru/img/pnas.jpg)

### Using Deep Learning and Google Street View to Estimate the Demographic Makeup of Neighborhoods Across the United States

Timnit Gebru, Jonathan Krause, Yilun Wang, Duyun Chen, Jia Deng, Erez Lieberman Aiden, Li Fei-Fei

PNAS 2017

[PDF](http://www.pnas.org/content/114/50/13108) [Data](https://ai.stanford.edu/~tgeb

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