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Sharad Goel

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by Sharad Goel

5harad.com
We’re hiring multiple full-time software engineers at @comppolicylab.bsky.social! Our team builds and deploys technology to tackle pressing public problems, in education, the delivery of government benefits, and beyond. Please help us spread the word! careers.harvard.edu/job/software...
Software Engineer
careers.harvard.edu
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Reposted by Sharad Goel

comppolicylab.bsky.social
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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Reposted by Sharad Goel

jdgrossman.com
There is considerable debate over whether Asian American students are admitted to selective universities at lower rates than similar white peers. In new work with Sabina Tomkins, Lindsay Page, and @5harad.com, we explore this issue using nearly 700k college applications. 🧵 nber.org/papers/w31527
The Disparate Impacts of College Admissions Policies on Asian American Applicants Joshua Grossman, Sabina Tomkins, Lindsay Page, Sharad Goel There is debate over whether Asian American students are admitted to selective colleges and universities at lower rates than white students with similar academic qualifications. However, there have been few empirical investigations of this issue, in large part due to a dearth of data. Here we present the results from analyzing 685,709 applications from Asian American and white students to a subset of selective U.S. institutions over five application cycles, beginning with the 2015-2016 cycle. The dataset does not include admissions decisions, and so we construct a proxy based in part on enrollment choices. Based on this proxy, we estimate the odds that Asian American applicants were admitted to at least one of the schools we consider were 28% lower than the odds for white students with similar test scores, GPAs, and extracurriculars.
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Reposted by Sharad Goel

comppolicylab.bsky.social
(1/2) Our own Johann Gaebler and @5harad.com, w/coauthors @seanjwestwood.bsky.social & Shanto Iyengar, analyzed 50+ years of US TV news using a novel LLM-based classification system. They find a steep decline in in-depth political coverage & substantive reporting, while soft news & commercials rise.
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Reposted by Sharad Goel

alexchohlaswood.com
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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Reposted by Sharad Goel

madisoncoots.com
🚨 Excited to share our new article in @annualreviews.bsky.social. Working with Kristin Linn, @5harad.com, Amol Navathe, and Ravi Parikh, we examine the fairness debates of seven prominent and controversial healthcare algorithms.🧵 madisoncoots.com/files/racial...
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by Sharad Goel

5harad.com
There are, of course, lots of important caveats to this work. For example, the results change if we move to a resource-constrained setting. But we hope our work illustrates the importance of looking at utility, not simply model miscalibration, in these debates.
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by Sharad Goel

5harad.com
As a result, the overall clinical utility of using race in these models is often surprisingly small. It still might make sense to include race, but the benefits of doing so have probably been overstated.
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by Sharad Goel

5harad.com
And because people for whom decisions flip are necessarily close to the decision boundary, their *utility* for intervening vs. not is comparable. (In the shared decision-making settings we consider, the utility is 0 at the boundary, i.e., the boundary is set to be the point of indifference.)
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by Sharad Goel

5harad.com
But despite this miscalibration, *decisions* (e.g., to intervene in some way) based on race-aware vs. race-unaware risk estimates are largely the same, as very few individuals are close to the decision boundary.
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by Sharad Goel

5harad.com
We find that race-unaware risk estimates are indeed often *miscalibrated*, systematically over or underestimating risk for different groups. Such miscalibration is often cited as evidence that including race improves the quality of predictions for all groups.
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by Sharad Goel

5harad.com
Using race in medical risk assessments is hotly debated, with some arguing that doing so improves accuracy while others worry it reinforce pernicious attitudes. With @madisoncoots.com and colleagues, we identify a statistical twist that's been largely overlooked. 5harad.com/papers/race-...
Abstract for "A Framework
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Reposted by Sharad Goel

comppolicylab.bsky.social
Calling 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. Learn more and sign up by Dec. 16: zurl.co/WitA
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by Sharad Goel

5harad.com
Our virtual tutor, called PingPong, is based on ChatGPT. It's designed to help students learn, not simply give them answers to homework problems.

We'll help customize PingPong to your course content.

Instructors can view de-identified student interactions.

PingPong is multilingual.
1

by Sharad Goel

5harad.com
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.

Sign up by Dec. 16! pingpong.hks.harvard.edu/eduaccess
PingPong 🏓
pingpong.hks.harvard.edu
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by Sharad Goel

5harad.com
Naviance is widely used by high school students to help them decide where to apply to college. In our new PNAS paper, Sabina Tomkins, Josh Grossman, Lindsay Page and I show it can inadvertently dissuade qualified students from applying to selective schools. www.pnas.org/doi/10.1073/...
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by Sharad Goel

5harad.com
Our paper "The Measure and Mismeasure of Fairness" — long in draft form — is now out in JMLR! We show that common error rate measures are often misleading indicators of algorithmic bias, and argue it's better to evaluate algorithms by looking at their effects. 5harad.com/papers/fair-...
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by Sharad Goel

5harad.com
Call for papers! Behavioral Science & Policy is running a special issue on behavioral insights for AI policy. Great opportunity to reach policymakers.

Initial submissions just require a 500-word abstract by Dec. 1.

Please help us get the word out!

behavioralpolicy.org/wp-content/u...
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Reposted by Sharad Goel

alexchohlaswood.com
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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by Sharad Goel

5harad.com
We find that Asian American applicants — especially South Asians — are much less likely to be admitted to selective colleges than white students with similar test scores, GPAs, and extracurricular activities.

Much of the gap is due to geography and preferential treatment of legacy applicants.
jdgrossman.com
Josh Grossman @jdgrossman.com · Aug 8
There is considerable debate over whether Asian American students are admitted to selective universities at lower rates than similar white peers. In new work with Sabina Tomkins, Lindsay Page, and @5harad.com, we explore this issue using nearly 700k college applications. 🧵 nber.org/papers/w31527
The Disparate Impacts of College Admissions Policies on Asian American Applicants Joshua Grossman, Sabina Tomkins, Lindsay Page, Sharad Goel There is debate over whether Asian American students are admitted to selective colleges and universities at lower rates than white students with similar academic qualifications. However, there have been few empirical investigations of this issue, in large part due to a dearth of data. Here we present the results from analyzing 685,709 applications from Asian American and white students to a subset of selective U.S. institutions over five application cycles, beginning with the 2015-2016 cycle. The dataset does not include admissions decisions, and so we construct a proxy based in part on enrollment choices. Based on this proxy, we estimate the odds that Asian American applicants were admitted to at least one of the schools we consider were 28% lower than the odds for white students with similar test scores, GPAs, and extracurriculars.
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Reposted by Sharad Goel

eckles.bsky.social
New faculty position at the intersection of politics and computing at MIT
https://academicjobsonline.org/ajo/jobs/25219
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References

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