no code implementations • 6 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.
no code implementations • 9 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.
no code implementations • 3 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
no code implementations • 3 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
no code implementations • 22 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.
5 code implementations • 8 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.
no code implementations • 8 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.
no code implementations • 14 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.
12 code implementations • 5 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.
21 code implementations • 23 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.
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.
no code implementations • 7 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.
no code implementations • 7 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.
no code implementations • 22 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.