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Evaluation of the Kosovo Memory Book at Pristina
HRDAG’s Year in Review: 2023
HRDAG’s Year in Review: 2022
First Things First: Assessing Data Quality Before Model Quality.
Anita Gohdes and Megan Price (2013). Journal of Conflict Resolution, Volume 57 Issue 6 December 2013. © 2013 Journal of Conflict Resolution. All rights reserved. Reprinted with permission of SAGE. [online abstract]DOI: 10.1177/0022002712459708.
HRDAG’s Year in Review: 2020
HRDAG’s Year End Review: 2019
Coming soon: HRDAG 2019 Year-End Review
HRDAG’s Year End Review: 2018
To predict and serve?
Kristian Lum and William Isaac (2016). To predict and serve? Significance. October 10, 2016. © 2016 The Royal Statistical Society.
Beautiful game, ugly truth?
Megan Price (2022). Beautiful game, ugly truth? Significance, 19: 18-21. December 2022. © The Royal Statistical Society. https://doi.org/10.1111/1740-9713.01702
Gaza: Why is it so hard to establish the death toll?
HRDAG director of research Patrick Ball is quoted in this Nature article about how body counts are a crude measure of the war’s impact and more reliable estimates will take time to compile.
Want to know a police officer’s job history? There’s a new tool
NPR Illinois has covered the new National Police Index, created by HRDAG’s Tarak Shah, Ayyub Ibrahim of Innocence Project, and Sam Stecklow of Invisible Institute.
Truth and Reconciliation Commission of Perú, Final Report – General Conclusions.
Truth and Reconciliation Commission of Perú, Final Report – General Conclusions. Comisión de la verdad y reconciliación, 2003.
Download: Megan Price
Executive director Megan Price is interviewed in The New York Times’ Sunday Review, as part of a series known as “Download,” which features a biosketch of “Influencers and their interests.”
Rise of the racist robots – how AI is learning all our worst impulses
“If you’re not careful, you risk automating the exact same biases these programs are supposed to eliminate,” says Kristian Lum, the lead statistician at the San Francisco-based, non-profit Human Rights Data Analysis Group (HRDAG). Last year, Lum and a co-author showed that PredPol, a program for police departments that predicts hotspots where future crime might occur, could potentially get stuck in a feedback loop of over-policing majority black and brown neighbourhoods. The program was “learning” from previous crime reports. For Samuel Sinyangwe, a justice activist and policy researcher, this kind of approach is “especially nefarious” because police can say: “We’re not being biased, we’re just doing what the math tells us.” And the public perception might be that the algorithms are impartial.
The ghost in the machine
“Every kind of classification system – human or machine – has several kinds of errors it might make,” [Patrick Ball] says. “To frame that in a machine learning context, what kind of error do we want the machine to make?” HRDAG’s work on predictive policing shows that “predictive policing” finds patterns in police records, not patterns in occurrence of crime.
5 Questions for Kristian Lum
Kristian Lum discusses the challenges of getting accurate data from conflict zones, as well as her concerns about predictive policing if law enforcement gets it wrong.
What happens when you look at crime by the numbers
Kristian Lum’s work on the HRDAG Policing Project is referred to here: “In fact, Lum argues, it’s not clear how well this model worked at depicting the situation in Oakland. Those data on drug crimes were biased, she now reports. The problem was not deliberate, she says. Rather, data collectors just missed some criminals and crime sites. So data on them never made it into her model.”