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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: https://suny-bin.primo.exlibrisgroup.com/permalink/01SUNY_BIN/t7hqh5/alma9936864375204802

Benjamin, R. (2019). Race after technology : abolitionist tools for the new Jim code . Polity Press.
Ebook link: https://suny-bin.primo.exlibrisgroup.com/permalink/01SUNY_BIN/t7hqh5/alma9936763390004802

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: https://suny-bin.primo.exlibrisgroup.com/permalink/01SUNY_BIN/t7hqh5/alma990035264000204802

Criado-Perez, C. (2019). Invisible Women : Data Bias in a World Designed for Men . Abrams Press.
Bartle Stacks HQ1237 .C75 2019
EBook link: https://suny-bin.primo.exlibrisgroup.com/permalink/01SUNY_BIN/gsdmgn/alma9936824324504802

Bordalejo, B., & Risam, R. (2020). Intersectionality in Digital Humanities . Arc Humanities Press, https://doi.org/10.1515/9781641890519
Ebook link:https://suny-bin.primo.exlibrisgroup.com/permalink/01SUNY_BIN/t7hqh5/alma991059638576604801

Atrey, S. (2019). Intersectional Discrimination. In Intersectional Discrimination. Oxford University Press. https://doi.org/10.1093/oso/9780198848950.001.0001
https://suny-bin.primo.exlibrisgroup.com/permalink/01SUNY_BIN/1igql2k/cdi_proquest_ebookcentral_EBC5896597

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. 
https://doi.org/10.1016/j.socscimed.2020.113138 
https://www-sciencedirect-com.proxy.binghamton.edu/science/article/pii/S0277953620303579

Agénor, M. (2020). Future directions for incorporating intersectionality into quantitative population health research. American Journal of Public Health110(6), 803–806. https://doi-org.proxy.binghamton.edu/10.2105/AJPH.2020.305610
http://proxy.binghamton.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=143074822&site=ehost-live

Campbell, R., & Wasco, S. M. (2000). Feminist approaches to social science: epistemological and methodological tenets. American Journal of Community Psychology28(6), 773–791.
http://proxy.binghamton.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=510147657&site=ehost-live

D'Ignazio, C., & Klein, LF. (2020). Seven intersectional feminist principals for equitable and actionalble COVID-19 data. Big Data & Society, 7(2), n.p., https://doi-org.proxy.binghamton.edu/10.1177%2F2053951720942544
https://journals-sagepub-com.proxy.binghamton.edu/doi/10.1177/2053951720942544

Else-Quest, N. M., & Hyde, J. S. (2016). Intersectionality in quantitative psychological research. Psychology of Women Quarterly40(2), 155–170.
https://doi-org.proxy.binghamton.edu/10.1177/0361684316629797)

Ferguson, K.E.(2017). Feminist theory today. Annual Review of Political Science, 20(1). 269-286.
https://doi-org.proxy.binghamton.edu/10.1146/annurev-polisci-052715-111648

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:http://dx.doi.org.proxy.binghamton.edu/10.1007/s10708-019-10035-z
https://link-springer-com.proxy.binghamton.edu/article/10.1007/s10708-019-10035-z

Giest, S., & Samuels, A. (2020). ‘For good measure’: Data gaps in a big data world. Policy Sciences, 53(3), 559-569. doi:http://dx.doi.org.proxy.binghamton.edu/10.1007/s11077-020-09384-1
http://proxy.binghamton.edu/login?url=https://www-proquest-com.proxy.binghamton.edu/scholarly-journals/good-measure-data-gaps-big-world/docview/2432687602/se-2?accountid=14168

Data is plural archive: https://data.world/jsvine/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). http://polygraph.cool/films/. Data on speaking roles in movies.
https://github.com/matthewfdaniels/scripts/

Risky predictions. Data predicting criminal behavior in the Broward Co, FL. (Propublica)
https://github.com/propublica/compas-analysis

Intersectional Bias in machine learning
https://github.com/jaeyk/intersectional-bias-in-ml
Analyses twitter data to identify bias in race and gender language and statements.

The most characteristic words in pro- and anti-feminist tweets (Github)
https://github.com/Prooffreader/twitter_feminism_analysis

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

Towards Debiasing Sentence Representations
https://paperswithcode.com/paper/towards-debiasing-sentence-representations-1 (abstract)
https://github.com/pliang279/sent_debias (code)

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

Neutralizer
https://github.com/unscodst/Neutralizer

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.)
https://www.youtube.com/watch?v=sWP92i7JLlQ

Algorithmic Bias and Fairness: Crash Course AI #18, 12/19/2019 (12 min.)
https://www.youtube.com/watch?v=gV0_raKR2UQ
Created by CrashCourse:https://www.youtube.com/user/crashcourse/about)

Algorithmic Justice: Race, Bias, and Big Data, 4/19/2019 (1hr, 40 min.)
https://www.youtube.com/watch?v=fzvcToI0Wo4
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.)
https://www.youtube.com/watch?v=PFKSs0qH_wU
(subsequent chapter guides available on YouTube)

Safiya Noble | Challenging the Algorithms of Oppression, 6/15/2016 (1 hr.)
Personal Democracy Forum 2016, Keynote Address
https://www.youtube.com/watch?v=iRVZozEEWlE