Photo of Alex Chohlas-Wood.
Photo credit: Simon Leuthi
Photo of Alex Chohlas-Wood.
Photo credit: Simon Luethi
Hi! I'm the executive director of the Stanford Computational Policy Lab (SCPL). I received a PhD in computational social science from Stanford, and served as the director of analytics at the New York City Police Department. My work focuses on using technology and data science to support criminal justice reform. Here's what I've been up to recently:
  • Blind charging

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    With colleagues at SCPL, I helped design and implement a race-blind charging algorithm. Our tool automatically masks race-related information in incident narratives to reduce the influence of race on charging decisions, as I describe on Twitter here. We piloted the algorithm at the San Francisco District Attorney’s office, and it’s now in daily use at the Yolo County District Attorney’s office near Sacramento. Our paper on the project was included in the 2021 Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. Blind charging is the subject of a new law in California, AB 2778, that mandates the use of blind charging across the state by 2025. Blind charging has been covered in numerous press articles.
  • Disparate impact in policing

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    In a paper, my colleagues and I described how data analysis can identify and measure racially disparate impacts in police stop policies, complementing other research which investigates whether bias exists in individual stops. We gave applied examples from a handful of major cities across the U.S., including Nashville, New York, Chicago, and Philadelphia. Our paper was published in the University of Chicago Law Review, and I wrote a Twitter thread about it here.
  • COVID-19 oriented reforms

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    In a Washington Post piece, I argued (with colleagues from SCPL) that the dramatic—but temporary, and patchwork—criminal justice reforms enacted in response to COVID-19 should be made permanent and expanded across the country.
  • Risk assessment instruments

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    I wrote a briefer on the potential advantages and risks of using risk assessment instruments in criminal justice settings for the Brookings Institute’s “AI and Bias” series on fairness in algorithms.
  • Patternizr

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    I helped design and deploy a tool used by detectives at NYPD to discover groups of related crimes. We described our approach in a published paper, including our effort to demonstrate the tool does not penalize any specific race group. The paper was published in the INFORMS Journal of Applied Analytics. Patternizr was featured in several articles.
  • Nashville police department

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    Our team at SCPL worked with the city of Nashville to demonstrate that traffic stops were an ineffective tool for fighting crime. Since the release of our report, the department has reduced the use of traffic stops by 70%—an almost 90% reduction from their peak.
  • Auditron

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    I designed an algorithm for the NYPD that looked for crimes which were misclassified as felonies or misdemeanors. Likely misclassifications were sent to an internal team for auditing and correction. I presented my approach at NYU’s Tyranny of the Algorithm? Predictive Analytics & Human Rights conference.