Alex Chohlas-Wood

In an age of rapidly evolving AI, we are *just* starting to learn how we can properly regulate these powerful new technologies. While many new regulations will be well-intended, we won't know what really works until we try many different approaches.

This week at @brookings.edu, me, @chiraagbains.bsky.social, and Katie Kinsey argue for why states should take the lead on the regulation of AI in criminal justice settings, a high-stakes setting where effective regulation could not be more important.

In an age of rapidly evolving AI, we are *just* starting to learn how we can properly regulate these powerful new technologies. While many new regulations will be well-intended, we won't know what really works until we try many different approaches.

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)

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?

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.

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?

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.

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%.

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

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%.

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.

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%.

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.

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%.

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/)

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/)

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/)

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/)

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/)

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/)

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/)

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/)

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/)

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/)

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/)

It might seem wrong to exclude most Black and Hispanic residents like this. So what should we do? We could instead provide a ride to an equal share of court attendees from every neighborhood. (For researchers in ML fairness, this is akin to imposing demographic parity.) (6/)

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/)

So we’d have to choose who gets a ride. A natural way to do this would be to select people who: a) Wouldn’t attend normally, but would attend if provided a ride b) Live close to court, so the ride is cheap This would maximize court appearances given our budget. Seems good! (4/)

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/)

Imagine a government initiative that aims to improve court attendance rates. In other research, we’ve already shown that sending automated court date reminders can increase court appearance rates and reduce pretrial incarceration. (2/) bsky.app/profile/alex...