I’m Scar Winter Kelsey, a data analyst with the EDIT Program at Northwestern University. I invite my readers to investigate the double-edged sword of queer data collection and analysis and what it means for successful evaluation.
Queer representation in our data systems is becoming more prominent – but what does this mean for queer liberation? An increasing number of companies are letting users enter their pronouns, gender identity, and/or trans status on entry forms. This ranges from medical intake forms to bank forms. I see one of the benefits immediately: I feel safer pursuing medical care where the practitioners probably won’t misgender me. But, I feel safer knowing I’m not required to fill out these fields at all.
Institutions shouldn’t require these fields to proceed to the next step in their intake or sign-up process. Requiring pronouns, gender identity, or trans status forces folks into situations they may not feel comfortable navigating. I am all for collecting gender identity data… when it makes sense. As evaluators navigating an evolving digital landscape, we must acknowledge how mandatory queer data collection can reinforce oppressive systems.
With more options to be yourself comes the increased chance that data collectors will use your trans status nefariously. For decades, large banks have disproportionately denied Black homebuyers mortgages or lent them loans with higher interest rates than their white counterparts. These racist barriers to financial mobility exhibit a pattern we would likely see among queer and trans homebuyers —especially Black and brown trans home buyers – if lenders knew their status.
If queer data is collected at all, we need to evaluate the methodology. Traditional data collection practices insist on a binary methodology which denies queer fluidity. Forms which use radio buttons instead of multiple-choice buttons to select gender identities, for example, are limiting. My gender is non-binary. I also am transgender. If I see “transmasculine” as a gender identity option, I’m clicking on that one, too. If it makes sense to collect queer data, let folks select multiple identities.
Why? For one, analysts can explore what respondents need from you based on their identities. Do your transgender, non-binary femme customers want more size-inclusive clothing options in different styles? Do your trans male patients want your office to make it clear they are welcome to seek abortion services? Knowing who your clients are assigns institutions the responsibility to meet their needs in a sensitive, informed fashion.
For two, survey designers do not get to decide who qualifies as queer. In Kevin Guyan’s “For Queer Communities, Being Counted has Downsides,” he details how census leaders in Scotland enforced archaic and binary options for queer identity and outlines the impact this has on Scotland’s queer community: “The creation of two-tiered queer communities favors gays and lesbians who are monogamous and married, trans people who have legal paperwork to document their transition, and bisexuals who pick a side and stick with it.” Queerness is infinite, and attempts to mold it into binaries leave folks without recourse.
Queer data collection sounds nice on paper, but it is a double-edged sword. Evaluators must consider the purpose for collecting the data in the first place, the downstream impacts of this collection, and who is in the room designing these surveys in the first place. If a survey wants queer folks to out themselves, there need to be queer folks on the other side of the screen ready to advocate for them.
Rad Resources:
- Algorithmic Justice League
- Algorithms of Oppression by Dr. Safiya Noble
- Queer Data by Kevin Guyan
- Data for Black Lives
- Design Justice by Sasha Costanza-Chock
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