Intersectional Data Manifesto

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Intersectional Data Manifesto

Historical Background and Example

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


Kenji Yoshino's Covering