Talking about data equity can be tricky. Maybe you’ve been to a conference or a workshop where you encountered an idea, a tool or a process that you’re super excited about. You want to bring it up with your team on Monday but by then you’re a little hazy on the details or you can’t concisely describe the ideas that you were so taken with. You also know that in order to actually implement anything you’re excited about, you’re going to have to convince the people in charge and that can be tricky.
Here are a few pointers and a great resource to get you started.
1. Don’t attack.
Often correcting equity issues highlights important problems that have been causing harm or oppression. The urgency of that wrong can bring out a lot of passion in people who want to address it. It’s good to be passionate, and anger is often an appropriate response to these problems, but making change often means being tactical. If you’re trying to convince a colleague or boss about something, coming in with excitement for a potentially positive change might get that change faster than just pointing fingers.
2. Be clear.
This is where it helps to come prepared. Having the bullet points in front of you can help to deliver a complete and cohesive suggestion, rather than a patchy retelling of someone else’s talk. We All Count believes in passing the baton, i.e. a good idea is no good unless people can use it and explain it. That’s why we make “Talk to Your Boss Sheets” about some of our most requested concepts. These resources aren’t nearly in-depth enough to fully explain every concept, but they’re just the right consistency to get the conversation started on firm ground.
3. Start small.
Trying to make sweeping changes all in one go can be overwhelming and ineffective. It’s ok to introduce a big idea with just a single component of it. We All Count thinks that one of the biggest obstacles to increasing equity in data science right now is an unwillingness to take small, immediate actions even if you don’t have a master plan for every facet of your work. Do something today. Do another thing tomorrow.
4. You don’t even have to say the word ‘equity’.
Sometimes people are immediately resistant to ideas wrapped in the words “equity”, “justice”, “fairness”, etc. It can sound like adding an entirely new dimension to the project. It can sound like a headache. It can sound like it’s going to be expensive. Well, guess what, almost all improvements to the equity of a data process are also improvements to the accuracy, effectiveness, and usefulness of that project.
Equitable data science is just good data science. Identifying bias, subjective choices and their effects on your work is valuable even if you don’t frame it in an equity paradigm.
5. If you’re not ahead, you’re behind.
This is one of our favourite tricks when encouraging someone to make some equity changes: if someone else is doing it, then it’s tested, proven, and you’re falling behind; if you can’t find any examples of someone implementing the idea, then you get to be pioneers on the cutting edge of data equity. We find both contexts to be pretty good for enticing people into making equity changes.