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Data Feminism Course Resources: Home

Books, Articles, and sample datasets for supporting Data Feminism in university courses

Curated Course Resources for Data Feminism

The following is a curated list of books related to Data Feminism. Library holdings information is included for print and electronic versions of titles.

D’Ignazio, C., & Klein, L. (2021). Data feminism . The MIT Press.
Ebook link:

Benjamin, R. (2019). Race after technology : abolitionist tools for the new Jim code . Polity Press.
Ebook link:

Broussard, M. (2018). Artificial unintelligence : how computers misunderstand the world . The MIT Press.
Print version call number: Bartle Stacks QA76.9.C66 B787 2018

Noble, S. (2018). Algorithms of oppression : how search engines reinforce racism . New York University Press.
Ebook link:

Criado-Perez, C. (2019). Invisible Women : Data Bias in a World Designed for Men . Abrams Press.
Bartle Stacks HQ1237 .C75 2019
EBook link:

Bordalejo, B., & Risam, R. (2020). Intersectionality in Digital Humanities . Arc Humanities Press,
Ebook link:

Atrey, S. (2019). Intersectional Discrimination. In Intersectional Discrimination. Oxford University Press.

Abrams, J. A., Tabaac, A., Jung, S., & Else-Quest, N. M. (2020). Considerations for employing intersectionality in qualitative health research. Social Science & Medicine258, n.p.

Agénor, M. (2020). Future directions for incorporating intersectionality into quantitative population health research. American Journal of Public Health110(6), 803–806.

Campbell, R., & Wasco, S. M. (2000). Feminist approaches to social science: epistemological and methodological tenets. American Journal of Community Psychology28(6), 773–791.

D'Ignazio, C., & Klein, LF. (2020). Seven intersectional feminist principals for equitable and actionalble COVID-19 data. Big Data & Society, 7(2), n.p.,

Else-Quest, N. M., & Hyde, J. S. (2016). Intersectionality in quantitative psychological research. Psychology of Women Quarterly40(2), 155–170.

Ferguson, K.E.(2017). Feminist theory today. Annual Review of Political Science, 20(1). 269-286.

Gardner, Z., Mooney, P., De, S. S., & Dowthwaite, L. (2020). Quantifying gendered participation in OpenStreetMap: Responding to theories of female (under) representation in crowdsourced mapping. GeoJournal, 85(6), 1603-1620. doi:

Giest, S., & Samuels, A. (2020). ‘For good measure’: Data gaps in a big data world. Policy Sciences, 53(3), 559-569. doi:

Data is plural archive:
Lots of links to small datasets for exercises on lack of diversity, representation, etc.

Some examples from the archive:

He said, she said (less). Data on speaking roles in movies.

Risky predictions. Data predicting criminal behavior in the Broward Co, FL. (Propublica)

Intersectional Bias in machine learning
Analyses twitter data to identify bias in race and gender language and statements.

The most characteristic words in pro- and anti-feminist tweets (Github)

Uses twitter data to classify tweets as pro and anti feminist.

Towards Debiasing Sentence Representations (abstract) (code)

This project dataset contains code for removing bias from BERT representations and evaluating bias level in BERT representations.


Helps create a gender and racial neutral browsing experience by removing gender and racial specific pronouns and replacing them with more neutral pronouns. 

Professor Kimberlé Crenshaw Defines Intersectionality, 9/16/2016 (12 min.)

Algorithmic Bias and Fairness: Crash Course AI #18, 12/19/2019 (12 min.)
Created by CrashCourse:

Algorithmic Justice: Race, Bias, and Big Data, 4/19/2019 (1hr, 40 min.)
Hosted by the Data Science Initiative and the Center for the Study of Race and Ethnicity in America at Brown University.
Meredith Broussard is the first speaker.

Data Feminism Reading Group, Chapter 1, 4/18/2020 (1 hr.)
(subsequent chapter guides available on YouTube)

Safiya Noble | Challenging the Algorithms of Oppression, 6/15/2016 (1 hr.)
Personal Democracy Forum 2016, Keynote Address