How can AI improve the New York Police Department and the justice system as a whole?
A perspective from CCJ Task Force on AI member @alexchohlaswood.com in @vitalcitynyc.bsky.social explores the potential opportunities, and risks, of the new technology: www.vitalcitynyc.org/how-to-deplo...
* According to the city's official AI inventory, which has its own issues — as I discuss in the piece!
www.vitalcitynyc.org/articles/how...
A 911 call dispatcher working in front of five open screens. CC By 2.0. Source: https://www.flickr.com/photos/143513894@N04/27188584211
Police departments nationwide are racing to adopt AI.
Yet the NYPD hasn't permanently adopted a new AI tool since 2016.*
That stagnation is a choice. And it's NYC's most vulnerable residents who pay the price.
In @vitalcitynyc.bsky.social this week, I lay out a better path forward. (Link below)
Thanks Nikhil! I have a copy if it’s taken down!
Learn more about how you can use 911 data to understand police deployment in our short blog post for ASA's Committee on Law and Justice Statistics:
community.amstat.org/lawandjustic...
A time series of overnight subway patrols between September 2019–June 2025. Subway patrols more than doubled after the NYPD's policy announcement, from about 7,500 train patrol calls per month before the announcement to over 15,000 calls per month after the announcement.
In a new blog post for the @amstatnews.bsky.social, John Hall and I make novel use of the city's 911 data to show that overnight train patrols more than doubled after the city announced its new policy in January.
A screenshot of NYC's Open Data portal listing NYPD's "Calls for Service (Historic)" dataset, with nearly 50 million rows.
But it turns out the information we need is already public, in the city's "Calls for Service"—a.k.a. 911—dataset!
You might think that you'd need detailed police deployment records to answer this question.
Getting this information via a public records request could take months, and may be denied by the department altogether.
Police officers face away from the camera toward a New York City subway train. The photo is taken through a fare gate. Photo credit: Metropolitan Transit Authority via CC 2.0.
In January, the NYPD said it would put two officers on every late-night subway car in New York City.
How can we know whether the NYPD kept its promise?
The Computational Policy Lab is growing! We’re hiring software engineers to build technology that makes government, education, and social programs more fair, effective, and data-driven. Know someone who'd be a great fit? Learn more: careers.harvard.edu/job/software...
And read more in a great piece from the Mercury News in San Jose (where our study was conducted):
www.mercurynews.com/2025/10/01/s...
Learn more about our study in this thread from two years ago:
bsky.app/profile/alex...
Reminders alone are unlikely to dramatically reduce overall jail populations, as jail stays for missed court dates are often short.
But stacking them with other small, common-sense reforms could substantially improve our justice system.
Reminders also help everyone by saving precious public resources instead of paying for these wasteful jail stays.
The reminders themselves cost about 60¢ per case—less than the costs of paying for someone’s arrest and incarceration, even for a single night.
We found that a broad swath of clients appeared to benefit from reminders—even people facing low-level charges.
Think of a DUI case: make a bad mistake one night, then forget a court date, and suddenly you’re in jail for a few days. A minor case just became much more serious.
Court date reminders may seem small, but they make a big difference.
Our study was conducted with public defender clients who can’t afford a lawyer.
These nudges are a huge boon for low-income clients, helping them avoid the high costs of a disruptive stay in jail.
Our study has now been peer reviewed—and it's officially out today in Science Advances!
It’s open access, which means it’s free for anyone to read.
www.science.org/doi/epdf/10....
A hand holding a smartphone displaying a court date reminder on screen.
Have you ever forgotten an important date—like a birthday for a loved one?
Now imagine if forgetting meant ending up in jail.
Two years ago, we ran a randomized experiment that found that text message reminders reduce jail stays for missed court dates by over 20%.
The ASA's Law & Justice Statistics committee is hosting an upcoming webinar featuring George Mohler. Join us on September 30 from 1–1:30pm ET! Register here: amstat.zoom.us/webinar/regi...
Calling all professors—we're enrolling new courses in our ongoing randomized study of AI in education!
Get free access to a customized virtual tutor, and receive an honorarium at the end of the semester if you participate. Details below.
Come join us and teach a great class this fall!
Great article about our work using generative AI to help prosecutors make race-blind charging decisions across California and beyond:
www.law360.com/articles/235...
(And I'll be teaching a course like this next spring at NYU so I'd love to hear what else you find!)
And another from Jochen Hartmann here: cms.mgt.tum.de/fileadmin/mg...
I came across two courses recently, one from @zjelveh.bsky.social here: zjelveh.github.io/teaching/ins...
Two students collaborating on a plot on a whiteboard.
Job alert!
We're hiring a clinical (teaching-based) Assistant Professor of Applied Statistics for Social Science Research at NYU!
Application review begins on February 10, and the position would start on September 1.
Apply here: apply.interfolio.com/162021
Yes to evaluating the *outcomes* of these systems rather than as a standalone algorithm! This is something that's been bothering me for a while about ML assisted decisions
Ultimately, we’ll likely achieve better outcomes if we think of algorithms as *policies* — and design them in a way that aims for the specific policy goals we desire.
(17/17)
bsky.app/profile/alex...
Learn more in our open-access paper, “Learning to be Fair: A Consequentialist Approach to Equitable Decision Making”, with @madisoncoots.com, Henry Zhu, Emma Brunskill, and @5harad.com!
pubsonline.informs.org/doi/10.1287/...
(16/)
Many studies have framed fairness as a mathematical problem, proposing axioms without considering the consequences.
In contrast, our approach:
- Focuses on outcomes
- Devises a computational framework for learning to be fair in an efficient and cost-effective manner
(15/)
