Lightnews — Scholar-powered news

Alex Chohlas-Wood @alexchohlaswood.com · 7d

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...

Study: Text reminders to South Bay public defender clients reduced jail time from court no-shows

Researchers at Stanford, Harvard, NYU piloted automated messaging to Santa Clara County defendants and achieved a 20% drop in bench warrants and pretrial incarceration.

www.mercurynews.com

Alex Chohlas-Wood @alexchohlaswood.com · 7d

Learn more about our study in this thread from two years ago:
bsky.app/profile/alex...

Alex Chohlas-Wood @alexchohlaswood.com · Oct 6

In a new randomized experiment at the Santa Clara County Public Defender Office, my colleagues and I found that text message reminders reduce *incarceration* for missed court dates by over 20%! More in the 🧵 below. alexchohlaswood.com/assets/paper... 1/11

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Alex Chohlas-Wood @alexchohlaswood.com · 7d

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.

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Alex Chohlas-Wood @alexchohlaswood.com · 7d

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.

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Alex Chohlas-Wood @alexchohlaswood.com · 7d

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.

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Alex Chohlas-Wood @alexchohlaswood.com · 7d

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.

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Alex Chohlas-Wood @alexchohlaswood.com · 7d

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....

Automated reminders reduce incarceration for missed court dates: Evidence from a text message experiment

You have to enable JavaScript in your browser's settings in order to use the eReader.

www.science.org

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Alex Chohlas-Wood @alexchohlaswood.com · 7d

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 · Sep 3

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 · Aug 4

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.

Computational Policy Lab at Harvard Kennedy School @comppolicylab.bsky.social · Aug 4

Instructors at 2- and 4-year colleges! Give your students free access to a virtual tutor optimized for learning by taking part in our study on using AI to improve education. Instructors receive a $1,000 honorarium.

Apps received by 8/8 receive priority: pingpong.hks.harvard.edu/eduaccess

PingPong 🏓

pingpong.hks.harvard.edu

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Alex Chohlas-Wood @alexchohlaswood.com · Jul 29

Come join us and teach a great class this fall!

Daphna Harel @daphna.bsky.social · Jul 29

NYC-based people - I'm looking for an adjunct instructor to teach an undergraduate course *this fall* at NYU called Power and Politics of Data. There's a lot of flexibility in what the course could look like, but I'm also happy to share ideas and the job ad. DM me if you think you're interested.

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Alex Chohlas-Wood @alexchohlaswood.com · Jul 21

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...

Seven Months In, Race-Blind Charging Faces Test In Calif. - Law360

In January, California adopted race-blind charging as a statewide policy, after a law passed in 2022 went into effect. Now, seven months into the program's statewide rollout, race-blind charging is sh...

www.law360.com

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Alex Chohlas-Wood @alexchohlaswood.com · Feb 14

(And I'll be teaching a course like this next spring at NYU so I'd love to hear what else you find!)

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Alex Chohlas-Wood @alexchohlaswood.com · Feb 14

And another from Jochen Hartmann here: cms.mgt.tum.de/fileadmin/mg...

cms.mgt.tum.de

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Alex Chohlas-Wood @alexchohlaswood.com · Feb 14

I came across two courses recently, one from @zjelveh.bsky.social here: zjelveh.github.io/teaching/ins...

Syllabus for INST 798/808: A.I.-Powered Research Assistants

personal description

zjelveh.github.io

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Alex Chohlas-Wood @alexchohlaswood.com · Jan 23

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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Reposted by Alex Chohlas-Wood

Hannah Li @hannahli.bsky.social · Jan 9

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

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Alex Chohlas-Wood @alexchohlaswood.com · Jan 8

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...

Alex Chohlas-Wood @alexchohlaswood.com · Jan 8

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 · Jan 8

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/)

Learning to Be Fair: A Consequentialist Approach to Equitable Decision Making | Management Science

pubsonline.informs.org

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Alex Chohlas-Wood @alexchohlaswood.com · Jan 8

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/)

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Alex Chohlas-Wood @alexchohlaswood.com · Jan 8

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 · Jan 8

The framework we designed uses contextual bandits and optimization to:
- Learn how people respond to rides, and then provide rides to people who need them—even while we’re still learning
- Equitably allocate rides by modeling preferences as parameters in a convex objective
(13/)

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Alex Chohlas-Wood @alexchohlaswood.com · Jan 8

But randomized controlled trials are costly in a couple ways.

First, people who would really benefit from a ride might be excluded if they’re randomized to a control arm.

Second, we might waste money on rides for people who don’t need transportation assistance.
(12/)

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Alex Chohlas-Wood @alexchohlaswood.com · Jan 8

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 · Jan 8

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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