Humanizing or Weaponizing Data – Treating People as Subject or Objects, get your data right

Earlier today my colleague Jondou Chen joined my organization’s advocacy and policy cohort to talk about weaponizing data (using a community’s own data against that community). Jondou is a mind-blowing presenter and partner in racial equity work, he also has a little goatee which if he stroked while presenting would make him look very professorial and add to his equity-aura.

Humor_axes_evil

Illustration by Grant Snider of Axes of Evil in graph form

One of the concepts he shared is how data can be used against a person or community. This happens when data from people is objectified, or turned into an object. Let me break this down into non-wonky language: Data starts with people. People are at the heart of data – people generate the research questions, they give information, people interpret data through quantitative or qualitative means. People have ‘agency’ and power over their data and are the subject in relationship to  their own data.

The problem becomes when we turn other people’s data into our object. That is we take and we use someone else’s data without allowing the subject (the people behind the data) to have power or control over it. Nicole in our group today observed: “By writing we turn subjects into objects,” as in who controls the narrative behind the data. What does it mean to do this and why and when do we trust them to tell an accurate story?

Turning a Subject into an Object

Over the past few months my organization has been partnering with a group of Chinese immigrant parents to help shape several policy asks. The longer back story is about a year ago my organization served as the backbone organization for a large community based survey on family engagement in schools. Many of these Chinese parents had previously taken the survey and were pleased to see we were closing the feedback loop to share what their data showed. After presenting their data several of the parents began to share stories and talk about what they wanted to see happen next, they were continuing to be ‘subjects’ in relationship to their data and refused to become an object where others would define their experiences with the data.

In a reverse example, there are many times we’ve seen data used against communities. This Seattle Times article on library usage shows double-digit declines in the number of library visits made in South Seattle library branches. Affluent neighborhoods saw increases in library attendance.

The subject of the data are library patrons. The library system does not track race in their count of who is using the library; overall I would characterize their tracking as race neutral with the belief they are open and accessible to all (in another blog post we’ll unpack why access isn’t equity) and defenders of freedom of information, if user data isn’t collected it can’t be used for evil is one belief strand of librarians. When we turn the subject of the data, library patrons, into objects we strip away the story and other important data on why people of color may not be using the library.

When people of color have agency/power to control the narrative around their own data the questions become deeper and nuanced. Such as how many of the books in the library are written by authors of color? Do libraries in the south end (less affluent) have children’s librarians and programming for families that would increase patron counts. Is the programming culturally enriching and co-designed with the community? How many of the patrons have fines blocking them from borrowing books – a $15 fine (the threshold when an account is blocked) is less of a burden in affluent communities. How many immigrant families know how to get a library card and what paperwork will it take?

When we weaponize data against a community it sounds like this fictitious example: “Library usage is down in South Seattle. We need to make budget cuts, so let’s cut hours at the Rainier Beach branch because it has the lowest visit count and the highest percentage of fees on record.” Taking data in this way without allowing the subject, Rainier Beach library patrons, to have power over their data (e.g. usage rates, fees, staff availability) is turning them into the object of a policy decision.

This same weaponizing phenomenon is seen in so many other sectors. In education, we see it happen with achievement gap data, family engagement where families are blamed for not participating, English Language Learner programs, and disciple rates to name a few. Asian and Pacific Islander communities where API data is lumped together hiding the disparities within the race category and playing into the Asian Myth. In elections policy makers may use data to gerrymander districts by saying we need to even out people count versus looking at where communities reside and may want to stay together for political power or vice versa look at where certain communities congregate and redraw lines to give one community more political clout.

How Not to Objectify or Weaponize Data

It is important to actively work against weaponizing data and turning people into data objects. One way we can do that is to remember that data comes from humans and people have important stories behind their data.

Here are some questions to ask to ensure you’re keeping your data as human as possible:

  • Who controls the narrative around data? Is it the communities and people who gave you the data?
  • Reframe the question of “What data is being counted?” to “Whom [people] is being counted?”
  • Who controls the research funding and how is it being allocated? (Data projects are often backed with money, be honest and transparent with your funding sources and allocation.)
  • Create and maintain feedback loops with communities who participate. What are researchers missing? What are they mis-measuring? What are they misinterpreting?
  • Believe in and use qualitative data.
  • Ask yourself “What don’t I know?” and be humble in acknowledging “What I don’t know, I don’t know,” and be ok with not being the expert and in control of everything.

Posted by Erin Okuno, with special thanks to Jondou Chen, PhD, for dropping some serious knowledge. *Note for Jondou: I think half our grant report is written.

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