Timnit Gebru

According to our database, Timnit Gebru authored at least 24 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

The TESCREAL bundle: Eugenics and the promise of utopia through artificial general intelligence.

Timnit Gebru, Émile P. Torres

First Monday, April, 2024

Beyond Fairness in Computer Vision: A Holistic Approach to Mitigating Harms and Fostering Community-Rooted Computer Vision Research.

Timnit Gebru, Remi Denton

Found. Trends Comput. Graph. Vis., 2024

Community Driven Approaches to Research in Technology & Society CCC Workshop Report.

Suresh Venkatasubramanian, Timnit Gebru, Ufuk Topcu, Haley Griffin, Leah Namisa Rosenbloom, Nasim Sonboli

CoRR, 2024

2023

AI Art and its Impact on Artists.

Harry H. Jiang, Lauren Brown, Jessica Cheng, Mehtab Khan, Abhishek Gupta, Deja Workman, Alex Hanna, Johnathan Flowers, Timnit Gebru

Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 2023

2022

A Human Rights-Based Approach to Responsible AI.

Vinodkumar Prabhakaran, Margaret Mitchell, Timnit Gebru, Iason Gabriel

CoRR, 2022

2021

Datasheets for datasets.

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

Commun. ACM, 2021

Constructing a Visual Dataset to Study the Effects of Spatial Apartheid in South Africa.

Raesetje Sefala, Timnit Gebru, Nyalleng Moorosi, Luzango Mfupe, Richard Klein

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?

Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, Shmargaret Shmitchell

Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

2020

Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning.

Timnit Gebru

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing.

Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, Parker Barnes

Proceedings of the FAT* '20: Conference on Fairness, 2020

Lessons from archives: strategies for collecting sociocultural data in machine learning.

Eun Seo Jo, Timnit Gebru

Proceedings of the FAT* '20: Conference on Fairness, 2020

Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing.

Inioluwa Deborah Raji, Timnit Gebru, Margaret Mitchell, Joy Buolamwini, Joonseok Lee, Emily Denton

Proceedings of the AIES '20: AAAI/ACM Conference on AI, 2020

Diversity and Inclusion Metrics in Subset Selection.

Margaret Mitchell, Dylan K. Baker, Nyalleng Moorosi, Emily Denton, Ben Hutchinson, Alex Hanna, Timnit Gebru, Jamie Morgenstern

Proceedings of the AIES '20: AAAI/ACM Conference on AI, 2020

2019

Oxford Handbook on AI Ethics Book Chapter on Race and Gender.

Timnit Gebru

CoRR, 2019

iCassava 2019Fine-Grained Visual Categorization Challenge.

Ernest Mwebaze, Timnit Gebru, Andrea Frome, Solomon Nsumba, Jeremy Tusubira

CoRR, 2019

Detecting Bias with Generative Counterfactual Face Attribute Augmentation.

Emily Denton, Ben Hutchinson, Margaret Mitchell, Timnit Gebru

CoRR, 2019

Model Cards for Model Reporting.

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

Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019

2018

Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification.

Joy Buolamwini, Timnit Gebru

Proceedings of the Conference on Fairness, Accountability and Transparency, 2018

2017

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

Proc. Natl. Acad. Sci. USA, 2017

Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US.

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

CoRR, 2017

Fine-Grained Recognition in the Wild: A Multi-task Domain Adaptation Approach.

Timnit Gebru, Judy Hoffman, Li Fei-Fei

Proceedings of the IEEE International Conference on Computer Vision, 2017

Scalable Annotation of Fine-Grained Categories Without Experts.

Timnit Gebru, Jonathan Krause, Jia Deng, Li Fei-Fei

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 2017

Fine-Grained Car Detection for Visual Census Estimation.

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

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2014

Learning Features and Parts for Fine-Grained Recognition.

Jonathan Krause, Timnit Gebru, Jia Deng, Li-Jia Li, Li Fei-Fei

Proceedings of the 22nd International Conference on Pattern Recognition, 2014