David Colarusso

David Colarusso

David Colarusso is an attorney, software engineer, and former high school physics teacher living in the greater Boston area. Originally hired by the MA public defender agency as a staff attorney, he now serves as their data scientist. He is the author of a programming language for lawyers, QnA Markup, an award winning legal hacker, and an adjunct faculty member at Suffolk University Law School. He likes to build things: furniture, software, reasoned arguments… His latest creations can be found on his eponymous website. And of course, the opinions expressed in his posts are his and do not reflect those of his employers.

Portland’s Precrime Experiment and the Limits of Algorithms

Using data science to predict where crime might occur is problematic. When we have unfair metrics, we develop unfair algorithms.

Pattern Recognition: Regular Expressions and You

Regular expressions, (a/k/a regex), are a powerful tool that can help you automate routine tasks. Here is a guide on how to get started.

Learning to Code

How to Build a Law Bot

The final part of our series teaching lawyers to code. This time, you'll put together what you've learned to build a Twitter bot.

Learning to Code

Online Forms Meet Local Document Automation (Cut-and-Paste Coding)

You can learn to automate your own documents while learning to code. When you're done with this lesson, you'll know just enough to be dangerous.

Learning to Code

Hello, World! Should Attorneys Learn to Code?

Lawyers should definitely learn to code because both information and technology are the tools of the profession's trade. Here's the first in a three-part guide that will teach you how to code.

Life (2016 Short Fiction Contest Winner)

"Life," by David Colarusso, is the winner of our 2016 Short Fiction Contest.

Driverless Cars Poised to Undermine War on Drugs: A Dispatch from the Future

We keep hearing that driverless cars are the future. If that is true, what does that mean for the future of law?

Uncovering Big Bias with Big Data

What follows is the story of how I used Virginia court cases to discover what best predicts defendant outcomes: race or income.