Using Data To Reduce Inequality
The racism we deal with today in America has evolved to be a lot more elusive than it was during the Civil Rights Movement. You can see its effects by looking at the country in aggregate. If you’re nonwhite, you’re more likely to be poor. You’re disproportionately more likely to have a run-in with the police, and when you do, you’re disproportionately more likely to be found guilty if you are prosecuted, and if you are found guilty, your sentences for the same crimes tend to be harsher than they would be if you were white.
It’s easy to see that in aggregate. You just have to measure it. But it’s more difficult to look at it on a case by case basis, because usually if there’s some racial bias happening, there’s plenty of plausible deniability surrounding it. The individual matched the description of someone who was reported to have robbed a convenience store is common in police reports, even when the individual is innocent.
Surprisingly, a technique that Tim Ferriss describes in his book The Four-Hour Body could be applicable here. In his book, he describes the importance of tracking data to lose weight. He found that merely using a scale to measure your weight every day leads to noticeable weight loss, even in the absence of any other diet or exercise regimen. Just seeing that data and being reminded of it every day is enough to affect tons of tiny, subsconscious decisions, and it made a noticeable difference.
What if we started using this technique with police officers? What if every police officer was briefed every week with the number of arrests they made, the racial breakdown of the arrests, and the racial breakdown of the local population. Using some statistics, this data could be tabulated into a score that indicates the likelihood that the officer is racially profiling civilians.
We don’t even need to give them quotas or targets to hit. We don’t need to have anyone breathing down officers’ necks about it. Hell, we don’t even need to share that data with the public. Just show it to each individual officer, as a constant reminder of the consequences of decisions they might not even be aware of. It’s so easy to think anecdotally about how fair you are, but when you see numbers that quantify it, it gets harder to rationalize.
I’m a really big fan of this technique for self-improvement, and it can be applied to any number of things. I work on a team of software engineers, and we could use data like this to help identify whether we have gender biases causing our lack of women on the team. You can use tools like RescueTime to know how much time you really spend checking your email during the work day.
On the surface, it’s just data. Just some numbers that happen to be correlated with happenings in the world. Being able to turn them into a better world is a beautiful thing.