Algorithmic Fairness & Opacity Lecture Series

The Algorithmic Fairness & Opacity lecture series hosts speakers from industry and academia to engage in cross-disciplinary conversations related to fairness and opacity in algorithms. Lectures are sponsored by the Berkeley Algorithmic Fairness & Opacity Working Group (AFOG).

Upcoming Events

Previous Events

Tawana Petty
Apr 14, 2021, 2:00 pm to 3:00 pm
Tawana Petty responds to the racial bias baked into our data, digital, and technological systems
Apr 12, 2021, 10:30 am to 12:15 pm
Understanding the recent resistance to facial recognition technologies, to put this moment in conversation and context.
Ethan Zuckerman
Mar 15, 2021, 10:30 am to 11:30 am
Ethan Zuckerman explores a model for rethinking and rebuilding social media and digital institutions as public goods.
Oct 14 2020 - All-day event
A virtual conference exploring organized technology refusal from historical and contemporary vantage points
Madeleine Clare Elish
Feb 24, 2020, 4:10 pm to 5:30 pm
Madeleine Clare Elish uncovers the hidden labor behind automated aviation, driverless cars, small scale agriculture, grocery retail, and emergency healthcare.
Nov 25, 2019, 4:10 pm to 5:30 pm
How practitioners think about design for algorithmic systems and products.
Kate Starbird
Nov 18, 2019, 4:10 pm to 5:30 pm
Key insights for defending against strategic information operations.
Desmond U. Patton
Oct 18, 2019, 4:10 pm to 5:30 pm
The promises and challenges of using AI to help prevent violence based on social media content and engagement.
Oct 17, 2019, 12:59 pm to 5:30 pm
Dr. Ruha Benjamin explores a range of discriminatory designs that encode inequity.
Michelle R. Carney
Sep 23, 2019, 4:10 pm to 5:30 pm
As AI and ML shape technology, how do we ensure that products are designed by and for people?
May 13, 2019, 4:00 pm to 6:00 pm
A conversation about “humans in the loop” who invisibly deliver on-demand task-based services and the lives of the people paid to train artificial intelligence.
Eric Horvitz
Mar 20, 2019, 3:30 pm to 5:00 pm
Eric Horvitz is a technical fellow and director at Microsoft Research.
Hoyt Long
Feb 27, 2019, 4:10 pm to 5:30 pm
Machine learning algorithms are not neutral observers of the world, but see what they are trained to see.
Nov 29, 2018, 4:00 pm to 5:30 pm
The impacts of data mining, policy algorithms, and predictive risk models on poor and working-class people in America.
Issa Kohler-Hausmann
Oct 31, 2018, 4:10 pm to 5:30 pm
Issa Kohler-Hausmann studies the methodological and theoretical issues in stating and proving discrimination claims.
Christian Sandvig
Apr 30, 2018, 4:10 pm to 5:30 pm
What to do when software algorithms are designed for statistical fairness or legal compliance, but still seem unfair.
Jon Kleinberg
Mar 19, 2018, 4:10 pm to 5:30 pm
What does it mean for algorithmic classification to be fair, and how can we promote algorithmic fairness?
Angèle Christin
Feb 26, 2018, 4:10 pm to 5:30 pm
Angèle Christin documents a gap between the intended and actual effects of algorithms in two professions.
Dawn Nafus
Dec 4, 2017, 4:10 pm to 5:30 pm
Dawn Nafus is a senior research scientist at Intel Labs, where she researches cultures of quantification and health and environmental sensing.