Towards Justice, Equity and Accountability in AI

While there is now an increased focus on fairness-oriented methods of model and dataset development, much of this work is constrained by a purely technical understanding of “fairness”—an understanding that has come to mean parity of model performance across socio-demographic groups. However, this only offers a narrow way of understanding how machine learning technologies intersect with systems of oppression that structure their development and applicable use in the real world. In contrast to this approach, it is essential to approach machine learning technologies from a socio-technical lens, examining how marginalized communities are both excluded from their development and also negatively impacted by their deployment. By centering the perspectives and stories of communities that have been harmed by machine learning technologies and the dominant logics operative within this field, we hope to shift the focus away from singular technical understandings of fairness and towards justice, equity, and accountability.

Timnit Gebru (she/her) is a computer scientist whose expertise focuses on research that mitigates the negative impacts of Artificial Intelligence. She holds Bachelor's and Master's degrees in Electrical Engineering, and received a PhD from the Stanford Artificial Intelligence Laboratory. Timnit's doctoral research used large-scale publicly available images to gain sociological insight, and worked on computer vision problems that arise as a result. After obtaining her PhD, Timnit worked as a post-doctoral fellow at the FATE (Fairness, Accountability, Transparency and Ethics) group at Microsoft Research NYC. She then co-led Google AI's Ethical AI team until she was fired in December 2020 for raising concerns about discrimination at work. An advocate for an increased presence of Black people in technology, she co-founded Black in AI, a non-profit organization for Black Professionals who work in the field of Artificial Intelligence.

This lecture is part of the Coding Resistance lecture series:

September 24, 2021 | 3 pm CEST
Outing and Outsmarting Discriminating Algorithms
with Nakeema Stefflbauer

October 01, 2021 | 3 pm CEST
Back to the Future of the African Village
with Minna Salami

October 08, 2021 | 3 pm CEST
Gender and Technology Beyond W.E.I.R.D.
with Maryam Mustafa

October 15, 2021 | 3 pm CEST
Glimmering Opacities: From Queering The Map to QT.bot
with Lucas LaRochelle

October 22, 2021 | 3 pm CEST
Emancipation Through the Virtual
with Iyo Bisseck

October 30, 2021 | 4 pm CEST
Make Time to Take Time
with Morehshin Allahyari

November 05, 2021 | 3 pm CEST
Interdependence as a Political Technology
with Aimi Hamraie

November 12, 2021 | 3 pm CEST
Digital Colonialism and Palestinian Resistance
with Marwa Fatafta

November 19, 2021 | 6 pm CEST
Towards Justice, Equity and Accountability in AI
with Timnit Gebru