Bias in AI Reading Group

TDAI hosts monthly informal gatherings to discuss publications addressing racial and gender bias in algorithms and automated decision-making technologies. For 2023-24, the group is led by Dr. Harmony Bench (Dance), and held the first Friday of the month during Fall semester and second Friday of the month during Spring semester. Our free-ranging conversations explore societal ramifications and implications for researchers, educators and students.

All OSU faculty, staff and students are welcome.

Join us!

Fall 2023: First Fridays, 3:00-4:00 p.m.
Spring 2024: Second Fridays, 3:00-4:00 p.m.

Where: 300A Pomerene Hall or by Zoom

Email Dr. Harmony Bench at for the Zoom link or for a copy of this month's text.

Click here to join the Bias in AI reading group listserv

Next selection

Readings for Friday, Dec. 1:

AI in the Wild: Sustainability in the Age of Artificial Intelligence by Peter Dauvergne
With participants reading chapters according to their interest.

Suggest future titles to read and discuss (opens a shared Excel list)

Previous Selections

Davide Gunkel, Perspective on Ethics of AI: Philosophy

Carolyn Ashurst, et al, AI Ethics Statements: Analysis and Lessons Learnt from NeurIPS Broader Impact Statements

Marika Cifor and Patricia Garcia, et al, Feminist Data Manifest-NO

The Black Technical Object: On Machine Learning and the Aspiration of Black Being by Ramon Amaro (Sternberg Press, 2022)

Uncomputable: Play and Politics in the Long Digital Age by Alexander Galloway (Verso, 2021).

"Meme Wars: The Untold Story of the Online Battles Upending Democracy in America," by Joan Donovan (New York: Bloomsbury, 2022).

Louise Amoore, Cloud Ethics: Algorithms and the Attributes of Ourselves and Others (Duke University Press, 2020).

Dan McQuillan, Resisting AI: An Anti-facist Approach to Artificial Intelligence (Bristol University Press, 2022). 

Wendy Chun, Discriminating Data: Correlation, Neighborhoods, and the New Politics of Recognition (MIT Press, 2021).

Sarah Brayne, "The Criminal Law and law enforcement implications of big data." Annual Review of Law and Social Science, Vol. 14:293-308, October 2018.

Brian Jefferson, "Digitize and punish: computerized crime mapping and racialized carceral power in Chicago." Environment and Planning D: Society and Space, Vol. 35, Issue 5: 775–796, March 2017.

Brian Christian, The Alignment Problem: Machine Learning and Human Values (W.W. Norton & Co., 2020).

Caroline Criado Perez, Invisible Women: Data Bias in a World Designed for Men (Abrams Press, 2019).

Catherine D'Ignazio and Laura F. Klein< Data Feminism (MIT Press, 2020).

Michael Kearns and Aaron Roth, The Ethical Algorithm: The Science of Socially Aware Algorithm Design (Oxford University Press, 2020).

Safiya Umoja Noble, Algorithms of Oppression: How Search Engines Reinforce Racism (NYU Press, 2018).