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The Data Equity Framework is a process that helps you to identify the many key choices you’ll have to make in a data project and provides tools and strategies to help improve your choice-making to better align with your equity goals. The other day someone raised a common question during one of my workshops:

“That’s great and all, but your Framework assumes that we are empowered to actually make these choices… what if, like me, you aren’t?”

Improving something means changing something. If we want to improve the equity of our data work, we’ll need to change how we go about it and that can feel somewhere along the spectrum from hard to impossible for many of us.

However, do we really mean it when we say that we “don’t have a choice”? Or do we mean “we don’t have a choice that costs us nothing”, or “we don’t have a choice that doesn’t make us uncomfortable” or “we don’t have a choice that is easy/risk-free/requires no work on our part”. One of the biggest equity problems in data right now is that the burden and vulnerability of not making a better choice simply get shifted onto the most vulnerable or powerless people involved in your project.

So what I wanted to say during that workshop was: “Baloney you don’t have a choice!”. But that’s not very helpful. So, I wanted to write up a quick example of some of the many choices I’ve seen people make if they are willing to put just a toe over the narrowly drawn parameters within which they feel they’ve been imprisoned.

Let’s take a look at our hypothetical friend named Brad.

Brad works for a municipality and he’s responsible for administering, analyzing and reporting a yearly survey. He’s hip to some of the more egregious data equity issues and right away he notices that the “Race and Ethnicity” categories they’ve been using are super limited and reductionist and white-oriented. He wants to improve the categories in this important part of the survey, but he knows that those categories are like that in order to match the data collected by the state. The categories (and the funding that they are attached to) have come down from on high and though he’d like to improve it, what choice does he have?

Here are 10 things that Brad could do ordered from least to most bad-ass:

1. Brad asks if he can change the categories. Woah… turns out that a lot of our “lack of choice” is merely assumed. Maybe Brad assumed that the categories were set in stone, but when he asked his boss, turns out it’s not a big deal at all.

2. Brad doesn’t change the categories but when reporting on the data he clearly includes where those categories came from and why they were used. Brad’s not totally passing the buck here because he’s actually low-key campaigning for change by sneakily turning up the heat on a major gatekeeper to improving this issue for his municipality and others: the state. That way when people (rightly) have beef with the inequality of that category they have somewhere useful to point.

3. Brad keeps the original categories but then adds a better, more nuanced question as well. This way he can fulfil the requirements that are “making” him use this poorly designed question, but he can report on and make use of his much more effective, respectful and inclusive version as well!

4. Brad supplements with outside datasets to get a better picture of what’s really going on with race and ethnicity in relation to his question. Focussing on the goal, not the problem can really help to sidestep his perceived “immovable issues”.

5. Brad omits the category. Until a better question can be created, Brad decides that no data is better than openly insulting or misleading data.

6. Brad insists that they change the categories. Go Brad! Not content to simply ask about this issue, Brad brings whatever clout, charm and printed-off blog posts that he can muster to convince the people in his department that this category needs to be changed. It’s better for people. It’s better for science. It’s better for PR. He makes the case.

7. Brad campaigns to improve the categories. He knows that he’s not the only person dealing with this outdated mandated data collection tool. He consults with his counterparts from other cities, talks to the data people at the state level, approaches the other departments and external organizations in his city who rely on this data, and engages with the community to confirm just how bad the categories are… and he does it all basically by sending emails for an hour or two. If you don’t like the choices being “forced” on you, chances are you are not at all alone!

8. Brad suggests a solution. Rather than just pointing out a problem, Brad develops a better version of the question developed with community members or experts and pitches it at the city or state level, offering it up as the gift that it is! Maybe there isn’t time for it to make it into this year’s survey, but next year the “Brad” version is the statewide standard! Yay Brad!

9. Brad publicly apologizes. When creating the report, not only does he point toward who is requiring these garbage racial categories, he acknowledges its shortcomings. Vulnerable. Risky. Meaningful. Gets some change going… hopefully.

10. Brad quits his job. The nuclear option. Now, that’s not a recommended step in the Data Equity Framework and 99% of the time there’s a better solution. But I wanted to include this to put it in perspective: when you say you don’t have a choice, what you often mean is I don’t have a problem that I care about more than this job.

Data analysts and data workers of any kind hold enormously important positions in our societies. What we do and don’t accept; what standards we will or won’t adopt; who the system prioritizes; these decisions can have life and death repercussions, and always have equity repercussions for the people in our projects.

Some of Brad’s actions wouldn’t be easy or even successful. But improving data equity means improving power dynamics and it can feel a bit ridiculous when people ask me for a solution that requires no change in their work or behaviour. It’s like asking “how can I change the status quo without challenging the status quo?”. It’s like telling a suffragette: “hey, if you care about suffrage so much, why not just vote for it?”.

In every step of the data process, you can do something even if it’s not the radical transformation that it may deserve. Every little bit helps. If you ever feel like “you don’t have a choice” I challenge you to describe your situation on our data equity forum. I know that you’ll get help finding a solution and more importantly find out that you are not at all alone in feeling powerless or restricted. We’ve all been there. But from now on when you say to me “I don’t have a choice”, you’d better darn well have at least tried something.