Notes toward a post on intersectional data

Jamia Wilson, Latoya Peterson, and I had a great conversation earlier this year about an idea: “intersectional data.” We have recently returned to the idea and the time for me to write a bit about it is growing close. As a way of working toward that end, this is an idea gathering post. If you have items to share, please let me know!
From our previous conversations:

Intersectional Data Manifesto

While we affirm the value of theoretical frameworks, we also want to draw attention to the material, affective, economic, and social impacts of reductive data collection and interpretation. This is important, life-saving information.
Examples where non-intersectional data has negatively impacted people include the large gaps in medical testing (cardiac disease and treatment).

• Forensic Science Learning from Sports Medicine
• Disparate impact: disparate impact is a legal theory of liability under several federal civil rights laws, including Title VII of the 1964 Civil Rights Act. It allows plaintiffs to challenge practices that, while facially neutral, disproportionately impact protected classes.To show why something has a disparate impact, plaintiffs often have to rely on statistics. Without statistics that show why certain groups experience disparate impact based on intersections of multiple identities—trans women, black women, and older women are just a few examples—this theory of legal liability will never be extended to those groups. A disturbing example of how this works is the case Rogers v. American Airlines.

Feminist scholars and activists have long pointed to the critical importance of narrative and we re-affirm that observation.

Key points of an intersectional framework for data

• Insists that we cannot separate out the complexities of our identities, nor should we
  Existing concepts of multivariate data are insufficient because they don’t articulate the power relations that shape how we live, know, and are known.
• Is messy – we aren’t interested in “cleaning our data.” Data that does not reflect the realities of our identities erase those identities. It is also fundamentally inaccurate data, and when its used for any purpose, those effects are exponentially multiplied.
• Is sometimes incomplete, but in its messiness is moving toward completeness
• Not easy to obtain – some groups will not show up in a standard research sets
• Like queer/feminist code, may not always execute, but is still meaningful. In fact, if our data can’t be “crunched” with current methods, then perhaps we need new ones.
• Data is supposed to give insight – there is no reason to limit our insights because we are uncomfortable with asking for more clarity
• Is not only about about individual data sets. Intersectional data also applies to the collection of data, preservation, use, and re-use, and the ethics deployed in these processes

Resources:
Bowleg, L.. “When Black + Lesbian + Woman ≠ Black Lesbian Woman: The Methodological Challenges of Qualitative and Quantitative Intersectionality Research.” Sex Roles 59 (2008): 312-325.http://ird.crge.umd.edu/entry_display.php?id=194
Crenshaw, Kimberlé.  “Mapping the Margins: Intersectionality, Identity Politics, and Violence Against Women of Color”. Stanford Law Review 43.6 (1991): 1241–1299
Puar, Jasbir. “‘I would rather be a cyborg than a goddess’ Intersectionality, Assemblage, and Affective Politics” http://eipcp.net/transversal/0811/puar/en
Geek Feminism on “Intersectionality” http://geekfeminism.wikia.com/wiki/Intersectionality
On Algorithmic Culture
Galloway, Alexander. Gaming: Essays in Algorithmic Culture (2006)
Paul Dourish on Algorithmic Culture…http://arithmus.eu/?p=238
Ted Striphas interview
The Atlantic on AC 
Strehovec, Janez “E-Literary Text and New Media Paratexts”
McNeill, Joanne. “Facebook and Algorithmic Culture” 
Algorithmic Cultures conference
Talt, Julian. “Living in the Age of Algorithmic Cultures”
https://medium.com/futurists-views/algorithmic-culture-culture-now-has-two-audiences-people-and-machines-2bdaa404f643#.gmi68cyfs
New Atlantis piece
Auditing Algorithms Bibliography 

Random thoughts:
Are abstraction and intersectionality mutually incompatible?
All data is situated, just as all knowledge is situated.
Neutrality is a myth all the way down.
Where are good models of complex systems? (Bio, ecology, but also the rendering of multimedia…)