Getting Past Identity to What You Really Want

Too often in data science, we use identity categories.

We once were hired by clients involved in a youth mental health situation where they needed to target scarce resources (why the resources were scarce is an entirely other conversation for a different time….) at providing support to young people in our community who were at risk for mental health issues. The client organization had research that showed that one of the primary drivers of the mental health issues among the youth in the community was bullying. So they wanted to make resources available to those most likely to be experiencing bullying.

How were we going to know who was getting bullied?

The client organization was pretty sure that young people who were not “white and straight” would be most likely to experience the most bullying. So they were considering making the resources available to young people who completed an intake form and identified as either a “person or color” or “LGTBQ+”.

However, we strongly discouraged them from doing this. The unintended consequences of these categories is that we are actually doubling down on marginalization and stereotypes. Instead of us preconceiving of who is likely to be having what lived experience, we encouraged them to ask directly. They did, using…