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Indirect Sampling to Measure Conflict Violence: Trade-offs in the Pursuit of Data That Are Good, Cheap, and Fast
Romesh Silva and Megan Price. “Indirect Sampling to Measure Conflict Violence: Trade-offs in the Pursuit of Data That Are Good, Cheap, and Fast.” Journal of the American Medical Association. 306(5):547-548. 2011. © 2011 JAMA. All rights reserved.
Data ‘hashing’ improves estimate of the number of victims in databases
But while HRDAG’s estimate relied on the painstaking efforts of human workers to carefully weed out potential duplicate records, hashing with statistical estimation proved to be faster, easier and less expensive. The researchers said hashing also had the important advantage of a sharp confidence interval: The range of error is plus or minus 1,772, or less than 1 percent of the total number of victims.
“The big win from this method is that we can quickly calculate the probable number of unique elements in a dataset with many duplicates,” said Patrick Ball, HRDAG’s director of research. “We can do a lot with this estimate.”
Missing People in Casanare
Daniel Guzmán, Tamy Guberek, Amelia Hoover, and Patrick Ball (2007). “Missing People in Casanare.” Benetech. Also available in Spanish – “Los Desaparecidos de Casanare.”
Los asesinatos de líderes sociales que quedan fuera de las cuentas
Una investigación de Dejusticia y Human Rights Data Analysis Group concluyó que hay un subconteo en los asesinatos de líderes sociales en Colombia. Es decir, que el aumento de estos crímenes en 2016 y 2017 podría ser incluso mayor al reportado por las organizaciones y por las cifras oficiales.
Searching for Trends: Analyzing Patterns in Conflict Violence Data
Megan Price and Anita Gohdes (2014). Searching for Trends: Analyzing Patterns in Conflict Violence Data. Political Violence @ a Glance. © 2014 PV@G.
Hat-Tip from Guatemala Judges on HRDAG Evidence
Annual reports
Palantir Has Secretly Been Using New Orleans to Test Its Predictive Policing Technology
One of the researchers, a Michigan State PhD candidate named William Isaac, had not previously heard of New Orleans’ partnership with Palantir, but he recognized the data-mapping model at the heart of the program. “I think the data they’re using, there are serious questions about its predictive power. We’ve seen very little about its ability to forecast violent crime,” Isaac said.
Celebrating Women in Statistics
In her work on statistical issues in criminal justice, Lum has studied uses of predictive policing—machine learning models to predict who will commit future crime or where it will occur. In her work, she has demonstrated that if the training data encodes historical patterns of racially disparate enforcement, predictions from software trained with this data will reinforce and—in some cases—amplify this bias. She also currently works on statistical issues related to criminal “risk assessment” models used to inform judicial decision-making. As part of this thread, she has developed statistical methods for removing sensitive information from training data, guaranteeing “fair” predictions with respect to sensitive variables such as race and gender. Lum is active in the fairness, accountability, and transparency (FAT) community and serves on the steering committee of FAT, a conference that brings together researchers and practitioners interested in fairness, accountability, and transparency in socio-technical systems.
The Statistics of Genocide
Patrick Ball and Megan Price (2018). The Statistics of Genocide. Chance (special issue). February 2018. © 2018 CHANCE.
Working Where Statistics and Human Rights Meet
Robin Mejia and Megan Price (2018). Working Where Statistics and Human Rights Meet. Chance (special issue). February 2018. © 2018 CHANCE.
On or off the record? Detecting patterns of silence about death in Guatemala’s National Police Archive
Tamy Guberek and Margaret Hedstrom (2017). On or off the record? Detecting patterns of silence about death in Guatemala’s National Police Archive. Archival Science. 9 February 2017. © Springer. DOI 10.1007/s10502-017-9274-3.