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Update of Iraq and Syria Data in New Paper
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The Limits of Observation for Understanding Mass Violence.
Haiti
Hunting for Mexico’s mass graves with machine learning
“The model uses obvious predictor variables, Ball says, such as whether or not a drug lab has been busted in that county, or if the county borders the United States, or the ocean, but also includes less-obvious predictor variables such as the percentage of the county that is mountainous, the presence of highways, and the academic results of primary and secondary school students in the county.”
¿Quién le hizo qué a quién? Planear e implementar un proyecto a gran escala de información en derechos humanos.
Patrick Ball (2008). “¿Quién le hizo qué a quién? Planear e implementar un proyecto a gran escala de información en derechos humanos.” (originally in English at AAAS) Translated by Beatriz Verjerano. Palo Alto, California: Benetech.
Who Did What to Whom? Planning and Implementing a Large Scale Human Rights Data Project
Patrick Ball. Who Did What to Whom? Planning and Implementing a Large Scale Human Rights Data Project. © 1996 American Association for the Advancement of Science.
Syria’s status, the migrant crisis and talking to ISIS
In this week’s “Top Picks,” IRIN interviews HRDAG executive director Patrick Ball about giant data sets and whether we can trust them. “No matter how big it is, data on violence is always partial,” he says.
Testimonials
Violence in Blue
Patrick Ball. 2016. Granta 134: 4 March 2016. © Granta Publications. All rights reserved.
Weighting for the Guatemalan National Police Archive Sample: Unusual Challenges and Problems.”
Gary M. Shapiro, Daniel R. Guzmán, Paul Zador, Tamy Guberek, Megan E. Price, Kristian Lum (2009).“Weighting for the Guatemalan National Police Archive Sample: Unusual Challenges and Problems.”In JSM Proceedings, Survey Research Methods Section. Alexandria, VA: American Statistical Association.
Predictive policing tools send cops to poor/black neighborhoods
In this post, Cory Doctorow writes about the Significance article co-authored by Kristian Lum and William Isaac.
Weapons of Math Destruction
Weapons of Math Destruction: invisible, ubiquitous algorithms are ruining millions of lives. Excerpt:
As Patrick once explained to me, you can train an algorithm to predict someone’s height from their weight, but if your whole training set comes from a grade three class, and anyone who’s self-conscious about their weight is allowed to skip the exercise, your model will predict that most people are about four feet tall. The problem isn’t the algorithm, it’s the training data and the lack of correction when the model produces erroneous conclusions.
A better statistical estimation of known Syrian war victims
Researchers from Rice University and Duke University are using the tools of statistics and data science in collaboration with Human Rights Data Analysis Group (HRDAG) to accurately and efficiently estimate the number of identified victims killed in the Syrian civil war.
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Using records from four databases of people killed in the Syrian war, Chen, Duke statistician and machine learning expert Rebecca Steorts and Rice computer scientist Anshumali Shrivastava estimated there were 191,874 unique individuals documented from March 2011 to April 2014. That’s very close to the estimate of 191,369 compiled in 2014 by HRDAG, a nonprofit that helps build scientifically defensible, evidence-based arguments of human rights violations.