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DATNAV: New Guide to Navigate and Integrate Digital Data in Human Rights Research
DatNav is the result of a collaboration between Amnesty International, Benetech, and The Engine Room, which began in late 2015 culminating in an intense four-day writing sprint facilitated by Chris Michael and Collaborations for Change in May 2016. HRDAG consultant Jule Krüger is a contributor, and HRDAG director of research Patrick Ball is a reviewer.
Press Release, Timor-Leste, February 2006
Justice by the Numbers
Wilkerson was speaking at the inaugural Conference on Fairness, Accountability, and Transparency, a gathering of academics and policymakers working to make the algorithms that govern growing swaths of our lives more just. The woman who’d invited him there was Kristian Lum, the 34-year-old lead statistician at the Human Rights Data Analysis Group, a San Francisco-based non-profit that has spent more than two decades applying advanced statistical models to expose human rights violations around the world. For the past three years, Lum has deployed those methods to tackle an issue closer to home: the growing use of machine learning tools in America’s criminal justice system.
All the Dead We Cannot See
Ball, a statistician, has spent the last two decades finding ways to make the silence speak. He helped pioneer the use of formal statistical modeling, and, later, machine learning—tools more often used for e-commerce or digital marketing—to measure human rights violations that weren’t recorded. In Guatemala, his analysis helped convict former dictator General Efraín Ríos Montt of genocide in 2013. It was the first time a former head of state was found guilty of the crime in his own country.
HRDAG To Join the Partnership on AI
How Causal Analysis Confirmed Impact of Cash Bail on Verdicts
Reflections: The People Who Make the Data
Pulling the Plug: Network Disruptions and Violence in the Syrian Conflict
Patrick Ball Honored with Degree at Claremont Graduate University
Asia
Funding
Lies, Damned Lies and Official Statistics
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.
The Use of Unstructured Data to Study Police Use of Force
Tarak Shah, Cristian Allen, Ayyub Ibrahim, Harlan Kefalas, and Bavo Stevens (2024). The Use of Unstructured Data to Study Police Use of Force. 5 December, 2024. CHANCE, 37(4), 18–23. https://doi.org/10.1080/09332480.2024.2434437
To predict and serve?
Kristian Lum and William Isaac (2016). To predict and serve? Significance. October 10, 2016. © 2016 The Royal Statistical Society.
Herb Spirer, 1925 – 2018
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
How Machine Learning Protects Whistle-Blowers in Staten Island
A Comparison of Marginal and Conditional Models for Capture–Recapture Data with Application to Human Rights Violations Data
Shira Mitchell, Al Ozonoff, Alan Zaslavsky, Bethany Hedt-Gauthier, Kristian Lum and Brent Coull (2013). A Comparison of Marginal and Conditional Models for Capture-Recapture Data with Application to Human Rights Violations Data. Biometrics, Volume 69, Issue 4, pages 1022–1032, December 2013. © 2013, The International Biometric Society. DOI: 10.1111/biom.12089.