Finding the Blank Spots in Big Data
Artists and designers are working to address a major problem for marginalized communities in the data economy: ‘If the data does not exist, you do not exist’
By Meg Miller
Mimi Onuoha is an artist who works mostly with algorithms, data sets, and digital systems, but her best known work may be a file cabinet. White, metal, and unassuming, it’s the kind that used to line the carpeted halls of office buildings before the advent of Google Drive and iCloud. Sliding open Onuoha’s cabinet reveals a column of familiar brownish-green folders, hooked at the sides and marked on top by plastic tabs. The handwritten labels include: “Publicly available gun trace data,” “Trans people killed or injured in instances of hate crime,” “Muslim mosques/communities surveilled by the FBI/CIA.” But when you open any one of the folders, there’s nothing inside.
This is Onuoha’s Library of Missing Datasets, a physical catalog of digital absence. She created the piece in 2016 (and a second version in 2018), after realizing that even with all of the esoteric, eccentric datasets you can find online — every word in the Broadway musical Hamilton, a yearly estimate of hotdogs eaten by Americans on the 4th of July — there’s a lot of urgent, necessary data that’s suspiciously missing. “In spaces that are oversaturated with data, there are blank spots where there’s nothing collected at all,” she says in a video for Data & Society. “When you look into them, you start to realize that they almost universally intersect with the interests of the most vulnerable.”
How often do we think of data as missing? Data is everywhere — it’s used to decide what products to stock in stores, to determine which diseases we’re most at risk for, to train AI models to think more like humans. It’s collected by our governments and used to make civic decisions. It’s mined by major tech companies to tailor our online experiences and sell to advertisers. As our data becomes an increasingly valuable commodity — usually profiting others, sometimes at our own expense — to not be…