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Guatemala: The Secret Files
Guatemala is still plagued by urban crime, but it is peaceful now compared to the decades of bloody civil war that convulsed the small Central American country. As he arrives in the capital, Guatemala City, FRONTLINE/World reporter Clark Boyd recalls, “When the fighting ended in the 1990s, many here wanted to move on, burying the secrets of the war along with hundreds of thousands of the dead and disappeared. But then, in July 2005, the past thundered back.”
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.
Experts Greet Kosovo Memory Book
On Wednesday, February 4, in Pristina, international experts praised the Humanitarian Law Centre’s database on victims of the Kosovo conflict, the Kosovo Memory Book. HRDAG executive director Patrick Ball is quoted in the article that appeared in Balkan Transitional Justice.
Civil War in Syria: The Internet as a Weapon of War
Suddeutsche Zeitung writer Hakan Tanriverdi interviews HRDAG affiliate Anita Gohdes and writes about her work on the Syrian casualty enumeration project for the UN Office of the High Commissioner for Human Rights. This article, “Bürgerkrieg in Syrien: Das Internet als Kriegswaffe,” is in German.
Data and Social Good: Using Data Science to Improve Lives, Fight Injustice, and Support Democracy
In this free, downloadable report, Mike Barlow of O’Reilly Media cites several examples of how data and the work of data scientists have made a measurable impact on organizations such as DataKind, a group that connects socially minded data scientists with organizations working to address critical humanitarian issues. HRDAG—and executive director Megan Price—is one of the first organizations whose work is mentioned.
Recognising Uncertainty in Statistics
In Responsible Data Reflection Story #7—from the Responsible Data Forum—work by HRDAG affiliates Anita Gohdes and Brian Root is cited extensively to make the point about how quantitative data are the result of numerous subjective human decisions. An excerpt: “The Human Rights Data Analysis Group are pioneering the way in collecting and analysing figures of killings in conflict in a responsible way, using multiple systems estimation.”
Can ‘predictive policing’ prevent crime before it happens?
HRDAG analyst William Isaac is quoted in this article about so-called crime prediction. “They’re not predicting the future. What they’re actually predicting is where the next recorded police observations are going to occur.”
Using Data to Reveal Human Rights Abuses
Profile touching on HRDAG’s work on the trial and conviction of Hissène Habré, its US Policing Project, data integrity, data archaeology and more.
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.
Documenting Syrian Deaths with Data Science
Coverage of Megan Price at the Women in Data Science Conference held at Stanford University. “Price discussed her organization’s behind-the-scenes work to collect and analyze data on the ground for human rights advocacy organizations. HRDAG partners with a wide variety of human rights organizations, including local grassroots non-governmental groups and—most notably—multiple branches of the United Nations.”
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.”
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.”
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.
Sobre fosas clandestinas, tenemos más información que el gobierno: Ibero
El modelo “puede distinguir entre los municipios en que vamos a encontrar fosas clandestinas, y en los que es improbable que vayamos a encontrar estas fosas”, explicó Patrick Ball, estadístico estadounidense que colabora con el Programa de Derechos Humanos de la Universidad Iberoamericana de la Ciudad de México.