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ORCIDTimnit Gebru
Person information
- affiliation: Distributed Artificial Intelligence Research institute (DAIR), USA
- affiliation (former): Google Research, Mountain View, CA, USA
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2020 – today
- 2023
[c14]Harry H. Jiang
, Lauren Brown, Jessica Cheng, Mehtab Khan, Abhishek Gupta, Deja Workman, Alex Hanna, Johnathan Flowers, Timnit Gebru:
AI Art and its Impact on Artists. AIES 2023: 363-374- 2022
- [i14]Vinodkumar Prabhakaran, Margaret Mitchell, Timnit Gebru, Iason Gabriel:
A Human Rights-Based Approach to Responsible AI. CoRR abs/2210.02667 (2022) - 2021
- [j2]Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna M. Wallach, Hal Daumé III, Kate Crawford:
Datasheets for datasets. Commun. ACM 64(12): 86-92 (2021) - [c13]Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, Shmargaret Shmitchell:
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? FAccT 2021: 610-623 - [c12]Raesetje Sefala, Timnit Gebru, Nyalleng Moorosi, Luzango Mfupe, Richard Klein:
Constructing a Visual Dataset to Study the Effects of Spatial Apartheid in South Africa. NeurIPS Datasets and Benchmarks 2021 - 2020
- [c11]Margaret Mitchell, Dylan K. Baker, Nyalleng Moorosi, Emily Denton, Ben Hutchinson, Alex Hanna, Timnit Gebru, Jamie Morgenstern:
Diversity and Inclusion Metrics in Subset Selection. AIES 2020: 117-123 - [c10]Inioluwa Deborah Raji, Timnit Gebru, Margaret Mitchell, Joy Buolamwini, Joonseok Lee, Emily Denton:
Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing. AIES 2020: 145-151 - [c9]Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, Parker Barnes:
Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing. FAT* 2020: 33-44 - [c8]Eun Seo Jo, Timnit Gebru:
Lessons from archives: strategies for collecting sociocultural data in machine learning. FAT* 2020: 306-316 - [c7]Timnit Gebru:
Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning. KDD 2020: 3609 - [i13]Inioluwa Deborah Raji, Timnit Gebru, Margaret Mitchell, Joy Buolamwini, Joonseok Lee, Emily Denton:
Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing. CoRR abs/2001.00964 (2020) - [i12]Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, Parker Barnes:
Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing. CoRR abs/2001.00973 (2020) - [i11]Margaret Mitchell, Dylan K. Baker, Nyalleng Moorosi, Emily Denton, Ben Hutchinson, Alex Hanna, Timnit Gebru, Jamie Morgenstern:
Diversity and Inclusion Metrics in Subset Selection. CoRR abs/2002.03256 (2020)
2010 – 2019
- 2019
- [c6]Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, Timnit Gebru:
Model Cards for Model Reporting. FAT 2019: 220-229 - [i10]Emily Denton, Ben Hutchinson, Margaret Mitchell, Timnit Gebru:
Detecting Bias with Generative Counterfactual Face Attribute Augmentation. CoRR abs/1906.06439 (2019) - [i9]Ernest Mwebaze, Timnit Gebru, Andrea Frome, Solomon Nsumba, Jeremy Tusubira:
iCassava 2019Fine-Grained Visual Categorization Challenge. CoRR abs/1908.02900 (2019) - [i8]Timnit Gebru:
Oxford Handbook on AI Ethics Book Chapter on Race and Gender. CoRR abs/1908.06165 (2019) - [i7]Eun Seo Jo, Timnit Gebru:
Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning. CoRR abs/1912.10389 (2019) - 2018
- [c5]Joy Buolamwini, Timnit Gebru:
Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. FAT 2018: 77-91 - [i6]Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna M. Wallach, Hal Daumé III, Kate Crawford:
Datasheets for Datasets. CoRR abs/1803.09010 (2018) - [i5]Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, Timnit Gebru:
Model Cards for Model Reporting. CoRR abs/1810.03993 (2018) - 2017
- [j1]Timnit Gebru, Jonathan Krause, Yilun Wang, Duyun Chen, Jia Deng, Erez Lieberman Aiden, Li Fei-Fei:
Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States. Proc. Natl. Acad. Sci. USA 114(50): 13108-13113 (2017) - [c4]Timnit Gebru, Jonathan Krause, Yilun Wang, Duyun Chen, Jia Deng, Li Fei-Fei:
Fine-Grained Car Detection for Visual Census Estimation. AAAI 2017: 4502-4508 - [c3]Timnit Gebru, Jonathan Krause, Jia Deng, Li Fei-Fei:
Scalable Annotation of Fine-Grained Categories Without Experts. CHI 2017: 1877-1881 - [c2]Timnit Gebru, Judy Hoffman, Li Fei-Fei:
Fine-Grained Recognition in the Wild: A Multi-task Domain Adaptation Approach. ICCV 2017: 1358-1367 - [i4]Timnit Gebru, Jonathan Krause, Yilun Wang, Duyun Chen, Jia Deng, Erez Aiden Lieberman, Li Fei-Fei:
Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US. CoRR abs/1702.06683 (2017) - [i3]Timnit Gebru, Judy Hoffman, Li Fei-Fei:
Fine-grained Recognition in the Wild: A Multi-Task Domain Adaptation Approach. CoRR abs/1709.02476 (2017) - [i2]Timnit Gebru, Jonathan Krause, Yilun Wang, Duyun Chen, Jia Deng, Li Fei-Fei:
Fine-Grained Car Detection for Visual Census Estimation. CoRR abs/1709.02480 (2017) - [i1]Timnit Gebru, Jonathan Krause, Jia Deng, Li Fei-Fei:
Scalable Annotation of Fine-Grained Categories Without Experts. CoRR abs/1709.02482 (2017) - 2014
- [c1]Jonathan Krause, Timnit Gebru, Jia Deng, Li-Jia Li, Li Fei-Fei:
Learning Features and Parts for Fine-Grained Recognition. ICPR 2014: 26-33
Coauthor Index
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