Data Feminism Course Resources: Home

Abrams, J. A., Tabaac, A., Jung, S., & Else-Quest, N. M. (2020). Considerations for employing intersectionality in qualitative health research. Social Science & Medicine, 258, 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 Health, 110(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 Psychology, 28(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 Quarterly, 40(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