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. Led by Dr. Nancy Ettlinger (geography) and held the last Thursday of the month, the free-ranging conversations explore societal ramifications and implications for researchers, educators and students.

All OSU faculty, staff and students are welcome.

Click here to join the Bias in AI reading group listserv

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


Join us!

Book Cover: Meme Wars by Joan Donovan

Our next selection will be Joan Donnovan's Meme Wars: The Untold Story the Online Battles Upending Democracy in America (New York: Bloomsbury, 2022). Please check back for a PDF link.

Next Meeting: Thursday, Feb. 23, 4-5 p.m., Derby 1186

You can also join by Zoom. Click here to register.

Please email Nancy Ettlinger at for a copy of this month's text.

Previous Selections



A still image from the documentary Coded Bias
Dr. Joy Buolamwini in "Coded Bias," a film directed by Shalini Kantayya (2020). A TDAI-hosted watch party of the film prompted the formation of the Bias in AI reading group.





(In descending order of most recent reads)

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). Click here for a PDF of the book

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).