694 results for search: %E3%80%8C%ED%95%AB%EB%AC%B4%EB%B9%84%E3%80%8D%20WWW%CD%BAHOTMOVIE%CD%BAPW%20%20%EC%B2%AD%EC%96%91%EB%8C%81%EC%9B%80%EC%A7%A4%20%EC%B2%AD%EC%96%91%EB%8C%81%EC%9B%90%EB%82%98%EC%9E%87%E2%97%8E%EC%B2%AD%EC%96%91%EB%8C%81%EC%9B%90%EB%B3%B8%C3%B2%EC%B2%AD%EC%96%91%EB%8C%81%EC%9C%A0%EC%B6%9C%E3%8A%AF%E3%82%85%E5%B5%BFslapping/feed/content/india/EnsaafPodcast-DescriptiveAnalysis.mp3
Using Cemetery Information in the Search for the Disappeared: Lessons from a Pilot Study in Rionegro, Antioquia
Tamy Guberek, Daniel Guzmán, and Beatriz Vejarano. “Using Cemetery Information in the Search for the Disappeared: Lessons from a Pilot Study in Rionegro, Antioquia.” In Methodological Proposals for Documenting and Searching for Missing Persons in Colombia. (Available in Spanish) © 2010 EQUITAS. All rights reserved.
Studying Millions of Rescued Documents: Sampling Plan at the Guatemalan National Police Archive (GNPA).
Daniel R. Guzmán, Tamy Guberek, Gary M. Shapiro, Paul Zador (2009). “Studying Millions of Rescued Documents: Sampling Plan at the Guatemalan National Police Archive (GNPA).” In JSM Proceedings, Survey Research Methods Section. Alexandria, VA: American Statistical Association.
Quantitative Data Analysis and Large-Scale Human Rights Violations: An Example of Applied Statistics at the Grassroots.
Romesh Silva. “Quantitative Data Analysis and Large-Scale Human Rights Violations: An Example of Applied Statistics at the Grassroots.” Gazette of the Australian Mathematical Society. Canberra (Australia). Volume 32, Number 2, May 2005.
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.
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.
Fosas clandestinas en México manifiestan existencia de crímenes de lesa humanidad
Patrick Ball, estadístico norteamericano, colabora con el Programa de Derechos Humanos de la Universidad Iberoamericana en una investigación sobre fosas clandestinas.
Tallying Syria’s War Dead
“Led by the nonprofit Human Rights Data Analysis Group (HRDAG), the process began with creating a merged dataset of “fully identified victims” to avoid double counting. Only casualties whose complete details were listed — such as their full name, date of death and the governorate they had been killed in — were included on this initial list, explained Megan Price, executive director at HRDAG. If details were missing, the victim could not be confidently cross-checked across the eight organizations’ lists, and so was excluded. This provided HRDAG and the U.N. with a minimum count of individuals whose deaths were fully documented by at least one of the different organizations. … “
The Untold Dead of Rodrigo Duterte’s Philippines Drug War
From the article: “Based on Ball’s calculations, using our data, nearly 3,000 people could have been killed in the three areas we analyzed in the first 18 months of the drug war. That is more than three times the official police count.”
Setting the Record Straight on Predictive Policing and Race
William Isaac and Kristian Lum (2018). Setting the Record Straight on Predictive Policing and Race. In Justice Today. 3 January 2018. © 2018 In Justice Today / Medium.
Nonprofits Are Taking a Wide-Eyed Look at What Data Could Do
In this story about how data are transforming the nonprofit world, Patrick Ball is quoted. Here’s an excerpt: “Data can have a profound impact on certain problems, but nonprofits are kidding themselves if they think the data techniques used by corporations can be applied wholesale to social problems,” says Patrick Ball, head of the nonprofit Human Rights Data Analysis Group.
Companies, he says, maintain complete data sets. A business knows every product it made last year, when it sold, and to whom. Charities, he says, are a different story.
“If you’re looking at poverty or trafficking or homicide, we don’t have all the data, and we’re not going to,” he says. “That’s why these amazing techniques that the industry people have are great in industry, but they don’t actually generalize to our space very well.”
PredPol amplifies racially biased policing
HRDAG associate William Isaac is quoted in this article about how predictive policing algorithms such as PredPol exacerbate the problem of racial bias in policing.
The causal impact of bail on case outcomes for indigent defendants in New York City
Kristian Lum, Erwin Ma and Mike Baiocchi (2017). The causal impact of bail on case outcomes for indigent defendants in New York City. Observational Studies 3 (2017) 39-64. 31 October 2017. © 2017 Institute of Mathematical Statistics.
New publication in BIOMETRIKA
Always Learning
Limitations of mitigating judicial bias with machine learning
Kristian Lum (2017). Limitations of mitigating judicial bias with machine learning. Nature. 26 June 2017. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. Nature Human Behavior. DOI 10.1038/s41562-017-0141.