Search Results for author: Timnit Gebru

Found 14 papers, 3 papers with code

A Human Rights-Based Approach to Responsible AI

no code implementations6 Oct 2022 Vinodkumar Prabhakaran, Margaret Mitchell, Timnit Gebru, Iason Gabriel

Research on fairness, accountability, transparency and ethics of AI-based interventions in society has gained much-needed momentum in recent years.

Ethics Fairness

Diversity and Inclusion Metrics in Subset Selection

no code implementations9 Feb 2020 Margaret Mitchell, Dylan Baker, Nyalleng Moorosi, Emily Denton, Ben Hutchinson, Alex Hanna, Timnit Gebru, Jamie Morgenstern

The ethical concept of fairness has recently been applied in machine learning (ML) settings to describe a wide range of constraints and objectives.


Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing

no code implementations3 Jan 2020 Inioluwa Deborah Raji, Timnit Gebru, Margaret Mitchell, Joy Buolamwini, Joonseok Lee, Emily Denton

Although essential to revealing biased performance, well intentioned attempts at algorithmic auditing can have effects that may harm the very populations these measures are meant to protect.

Computers and Society

Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing

no code implementations3 Jan 2020 Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, Parker Barnes

Rising concern for the societal implications of artificial intelligence systems has inspired a wave of academic and journalistic literature in which deployed systems are audited for harm by investigators from outside the organizations deploying the algorithms.

Computers and Society

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

no code implementations22 Dec 2019 Eun Seo Jo, Timnit Gebru

A growing body of work shows that many problems in fairness, accountability, transparency, and ethics in machine learning systems are rooted in decisions surrounding the data collection and annotation process.

BIG-bench Machine Learning Ethics +1

iCassava 2019 Fine-Grained Visual Categorization Challenge

5 code implementations8 Aug 2019 Ernest Mwebaze, Timnit Gebru, Andrea Frome, Solomon Nsumba, Jeremy Tusubira

Viral diseases are major sources of poor yields for cassava, the 2nd largest provider of carbohydrates in Africa. At least 80% of small-holder farmer households in Sub-Saharan Africa grow cassava.

Fine-Grained Visual Categorization

Oxford Handbook on AI Ethics Book Chapter on Race and Gender

no code implementations8 Aug 2019 Timnit Gebru

This includes standardization bodies determining what types of systems can be used in which scenarios, making sure that automated decision tools are created by people from diverse backgrounds, and understanding the historical and political factors that disadvantage certain groups who are subjected to these tools.

BIG-bench Machine Learning Decision Making +3

Image Counterfactual Sensitivity Analysis for Detecting Unintended Bias

no code implementations14 Jun 2019 Emily Denton, Ben Hutchinson, Margaret Mitchell, Timnit Gebru, Andrew Zaldivar

Facial analysis models are increasingly used in applications that have serious impacts on people's lives, ranging from authentication to surveillance tracking.

Attribute counterfactual +1

Model Cards for Model Reporting

12 code implementations5 Oct 2018 Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, Timnit Gebru

Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information.

BIG-bench Machine Learning

Datasheets for Datasets

23 code implementations23 Mar 2018 Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, Kate Crawford

The machine learning community currently has no standardized process for documenting datasets, which can lead to severe consequences in high-stakes domains.

BIG-bench Machine Learning

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

no code implementations ICCV 2017 Timnit Gebru, Judy Hoffman, Li Fei-Fei

While fine-grained object recognition is an important problem in computer vision, current models are unlikely to accurately classify objects in the wild.

Attribute Domain Adaptation +1

Fine-Grained Car Detection for Visual Census Estimation

no code implementations7 Sep 2017 Timnit Gebru, Jonathan Krause, Yi-Lun Wang, Duyun Chen, Jia Deng, Li Fei-Fei

In this work, we leverage the ubiquity of Google Street View images and develop a computer vision pipeline to predict income, per capita carbon emission, crime rates and other city attributes from a single source of publicly available visual data.

Scalable Annotation of Fine-Grained Categories Without Experts

no code implementations7 Sep 2017 Timnit Gebru, Jonathan Krause, Jia Deng, Li Fei-Fei

We present a crowdsourcing workflow to collect image annotations for visually similar synthetic categories without requiring experts.

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

no code implementations22 Feb 2017 Timnit Gebru, Jonathan Krause, Yi-Lun Wang, Duyun Chen, Jia Deng, Erez Lieberman Aiden, Li Fei-Fei

The United States spends more than $1B each year on initiatives such as the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors.

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