Alex Chohlas-Wood
@alexchohlaswood.com
Assistant professor at NYU interested in computational public policy and the criminal justice system. Co-direct @comppolicylab.bsky.social.📍NYC 🏳️🌈 alexchohlaswood.com
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.
October 30, 2025 at 2:31 PM
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.
But it turns out the information we need is already public, in the city's "Calls for Service"—a.k.a. 911—dataset!
October 30, 2025 at 2:31 PM
But it turns out the information we need is already public, in the city's "Calls for Service"—a.k.a. 911—dataset!
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?
How can we know whether the NYPD kept its promise?
October 30, 2025 at 2:31 PM
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?
How can we know whether the NYPD kept its promise?
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%.
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%.
October 1, 2025 at 6:09 PM
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%.
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...
September 3, 2025 at 4:06 PM
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...
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
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
January 23, 2025 at 3:59 PM
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
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
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/)
- 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/)
January 8, 2025 at 11:31 PM
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/)
- 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/)
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/)
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/)
January 8, 2025 at 11:31 PM
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/)
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/)
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/)
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/)
January 8, 2025 at 11:31 PM
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/)
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/)
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/)
Most people preferred an outcome other than demographic parity—even people in the same political party!
(9/)
January 8, 2025 at 11:31 PM
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/)
Most people preferred an outcome other than demographic parity—even people in the same political party!
(9/)
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/)
But “what feels fair” is ultimately a matter of personal preference, and depends on the exact tradeoff in question.
(8/)
January 8, 2025 at 11:31 PM
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/)
But “what feels fair” is ultimately a matter of personal preference, and depends on the exact tradeoff in question.
(8/)
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/)
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/)
January 8, 2025 at 11:31 PM
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/)
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/)
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/)
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/)
January 8, 2025 at 11:31 PM
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/)
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/)
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/)
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/)
January 8, 2025 at 11:31 PM
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/)
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/)
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/)
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/)
January 8, 2025 at 11:31 PM
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/)
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/)
If you want to be rewarded with a surprise when your R script finishes running, try adding this at the end:
November 17, 2024 at 6:06 PM
If you want to be rewarded with a surprise when your R script finishes running, try adding this at the end:
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
6/11
October 6, 2023 at 3:36 PM
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
6/11
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
5/11
October 6, 2023 at 3:35 PM
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
5/11