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)
Our next selection will be The Black Technical Object: On Machine Learning and the Aspiration of Black Being by Ramon Amaro (Sternberg Press, 2022).
Next Meeting: Thursday, Apr. 27, 4-5 p.m., Derby 1186
Please email Nancy Ettlinger at email@example.com for a Zoom Link for the meeting, or for a copy of this month's text.
(In descending order of most recent reads)
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). https://library.ohio-state.edu/search/t?SEARCH=cloud+ethics&searchscope=7
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. https://doi.org/10.1146/annurev-lawsocsci-101317-030839
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. https://doi.org/10.1177/0263775817697703
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).
Other Reads and Resources
- “Science-fiction master Ted Chiang explores the rights and wrongs of AI” (Science, 11/8/20)
- “Can We Make Our Robots Less Biased Than We Are?” (New York Times, 11/22/20)
- “Artificial Intelligence, Health Disparities, and COVID-19” (Undark, 7/27/20)