So I went out and built a phenomenal team of data scientists and researchers and statisticians to build a universal risk assessment tool,
so that every single judge in the United States of America can have an objective, scientific measure of risk.
In the tool that we've built, what we did was we collected 1.5 million cases from all around the United States,
from cities, from counties, from every single state in the country, the federal districts.
And with those 1.5 million cases, which is the largest data set on pretrial in the United States today,
we were able to basically find that there were 900-plus risk factors that we could look at to try to figure out what mattered most.
And we found that there were nine specific things that mattered all across the country and that were the most highly predictive of risk.
And so we built a universal risk assessment tool. And it looks like this.
As you'll see, we put some information in, but most of it is incredibly simple,
it's easy to use, it focuses on things like the defendant's prior convictions,
whether they've been sentenced to incarceration,
whether they've engaged in violence before, whether they've even failed to come back to court.
And with this tool, we can predict three things.
First, whether or not someone will commit a new crime if they're released.
Second, for the first time, and I think this is incredibly important,
we can predict whether someone will commit an act of violence if they're released.
And that's the single most important thing that judges say when you talk to them.
And third, we can predict whether someone will come back to court.
And every single judge in the United States of America can use it, because it's been created on a universal data set.