iiinova — where your digital self keeps evolving

Alex Chohlas-Wood @alexchohlaswood.com · 23/02/2026

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)

A 911 call dispatcher working in front of five open screens. CC By 2.0. Source: https://www.flickr.com/photos/143513894@N04/27188584211

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Alex Chohlas-Wood @alexchohlaswood.com · 30/10/2025

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 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.

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Alex Chohlas-Wood @alexchohlaswood.com · 30/10/2025

But it turns out the information we need is already public, in the city's "Calls for Service"—a.k.a. 911—dataset!

A screenshot of NYC's Open Data portal listing NYPD's "Calls for Service (Historic)" dataset, with nearly 50 million rows.

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Alex Chohlas-Wood @alexchohlaswood.com · 30/10/2025

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?

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.

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Alex Chohlas-Wood @alexchohlaswood.com · 01/10/2025

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%.

A hand holding a smartphone displaying a court date reminder on screen.

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Alex Chohlas-Wood @alexchohlaswood.com · 03/09/2025

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...

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Alex Chohlas-Wood @alexchohlaswood.com · 23/01/2025

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

Two students collaborating on a plot on a whiteboard.

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Alex Chohlas-Wood @alexchohlaswood.com · 08/01/2025

We use data from the Santa Clara County Public Defender to show that this approach would result in higher utility: - During the learning phase AND - After we stop learning! Of course, this approach applies in any resource-constrained setting, not just for rides to court! (14/)

A chart of regret vs. iteration. Random assignment methods accumulate a lot of regret over the course of an experiment, whereas bandit methods like UCB and Thompson sampling accumulate minimal regret.

A chart of performance vs. iteration. Bandit methods like UCB and Thompson sampling are quick to learn good policies, while other methods learn good policies more slowly.

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Alex Chohlas-Wood @alexchohlaswood.com · 08/01/2025

How could we make decisions like this in the real world? One approach would be to run a randomized controlled trial to learn how people respond to rides. We could then estimate the tradeoffs at hand, and choose an tradeoff that best reflects our preferences. (11/)

A Pareto curve, showing the tradeoff between helping people get to court and the proportion of people in a target group who get the benefit of rides. The chart also shows a slider bar indicating a survey response, with the selection corresponding to the point of maximum utility.

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Alex Chohlas-Wood @alexchohlaswood.com · 08/01/2025

This suggests that there’s no one-size-fits-all definition of fairness. Instead, we should make decisions in a way that reflects our preference for how to make difficult tradeoffs. (In practice, one could run a survey like the above to elicit preferences from people.) (10/)

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Alex Chohlas-Wood @alexchohlaswood.com · 08/01/2025

To illustrate, we asked 300 Americans how they would make this tradeoff. After explaining the problem, we let them choose their preferred outcome. Most people preferred an outcome other than demographic parity—even people in the same political party! (9/)

The graphic we showed survey respondents. There are five options presented, with various amounts of average spending per Black and White person. Each option also has an associated number of people who avoid jail, with the highest number associated with more spending per White person than Black person.

A bar chart of survey responses, split by party affiliation.

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Alex Chohlas-Wood @alexchohlaswood.com · 08/01/2025

Fortunately, we don’t have to follow only one of these approaches. We could instead balance between these approaches in a way that feels most fair. But “what feels fair” is ultimately a matter of personal preference, and depends on the exact tradeoff in question. (8/)

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Alex Chohlas-Wood @alexchohlaswood.com · 08/01/2025

This would reduce disparities in who gets a ride. But there would be real drawbacks! - We’d pay for longer rides to court, so - We’d provide fewer rides overall, so - More people would go to jail for missing court. In other words, there’s an inherent tradeoff at play. (7/)

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Alex Chohlas-Wood @alexchohlaswood.com · 08/01/2025

But there’s a catch! Take Boston as an example. In prioritizing cheap + short rides, imagine that we drew from people who lived close to the courthouse (like in the map). By trying to be efficient with our budget, we’d exclude many Black + Hispanic residents of Boston. (5/)

A map of the Boston area, showing residents in different colors by their race/ethnicity. A star is in the center of the map surrounded by a circle; most of the people in the circle are white residents, with some Asian and some Hispanic residents as well. A large population of Black and Hispanic residents sits south of the city center and is outside of the circle.

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Alex Chohlas-Wood @alexchohlaswood.com · 08/01/2025

What if we wanted to improve court attendance rates even more? One possible initiative would be providing people with free rides to court. But with a limited budget, we wouldn’t have enough funding to give everyone a ride. (3/)

An image of two women appearing to shake hands outside of a car.

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Alex Chohlas-Wood @alexchohlaswood.com · 08/01/2025

NEW in Management Science! My coauthors and I came up with a new consequentialist approach to designing equitable algorithms. Instead of imposing fairness criteria on an algorithm (like equal false negative rates), we aim for good outcomes. More in the 🧵 below! (1/)

A screenshot of the first page of our paper, Learning to Be Fair, showing the title and abstract.

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Alex Chohlas-Wood @alexchohlaswood.com · 17/11/2024

If you want to be rewarded with a surprise when your R script finishes running, try adding this at the end:

This shows a snippet of R code that says "beepr::beep(8)".

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Alex Chohlas-Wood @alexchohlaswood.com · 06/10/2023

Over the study period, we found that reminders reduced jail time for missed court dates by 20% when compared against no reminders—from 6.2% of clients in the control condition to 4.8% of clients in the reminder condition. 6/11

A hand holding a smartphone displaying the reminders on screen.

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Alex Chohlas-Wood @alexchohlaswood.com · 06/10/2023

Our study included over 5,700 public defender clients who had court dates between May 2022 and August 2023. Half of these clients were randomly assigned to receive text message reminders, while the other half consisted of a control group and received no reminders. 5/11

A diagram of the different messages that people may receive to remind them of upcoming court dates.

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