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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
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
Patrick Ball and Jana Asher (2002). “Statistics and Slobodan: Using Data Analysis and Statistics in the War Crimes Trial of Former President Milosevic.” Chance, vol. 15, No. 4, 2002. Reprinted with permission ofChance. © 2002 American Statistical Association. All rights reserved.
Violence against women in all its forms is a human rights violation. Most of our HRDAG colleagues are women, and for us, unfortunately, recent campaigns such as #metoo are unsurprising.
Maria Gargiulo has joined HRDAG as a Statistician.
On March 16, Kristen Yawitz joined the HRDAG team in the role of Foundation Relations and Strategy Lead.
In this
Granta article, HRDAG explains that neither the infectiousness nor the deadliness of the disease is set in stone.
HRDAG and our partners Data Cívica and the Iberoamericana University created a machine-learning model to predict which counties (municipios) in Mexico have the highest probability of unreported hidden graves. The predictions help advocates to bring public attention and government resources to search for the disappeared in the places where they are most likely to be found.
Context
For more than ten years, Mexican authorities have been discovering hidden graves (fosas clandestinas). The casualties are attributed broadly—and sometimes inaccurately—to the country’s “drug war,” but the motivations and perpetrators behind the mass murders ...
THis story might be about Racial Justice Act work with San Francisco Public Defender’s Office
I joined the Benetech Human Rights Program at essentially the same time that HRDAG did, coming to Benetech from years of analyzing data for large companies in the transportation, hospitality and retail industries. But the data that HRDAG dealt with was not like the data I was familiar with, and I was fascinated to learn about how they used the data to determine "who did what to whom." Although some of the methodologies were similar to what I had experience with in the for-profit sector, the goals and beneficiaries of the analyses were very different.
At Benetech, I was initially predominantly focused on product management for Martus, a free ...
Margot is a professor in the Department of Energy Resources Engineering at Stanford University, interested in computer simulation and mathematical analysis of engineering processes.
It was July of 2006, I’d spent five years working at a local human rights NGO in Bogotá, and I had reached retirement age. But then a whole new world opened up for me to discover. Tamy Guberek, then HRDAG Latin America coordinator, whom I had met at the NGO, approached me about becoming part of the HRDAG Colombia team as a research/administrative assistant. Over a cup of suitably Colombian coffee, the deal was quickly "signed.” My responsibilities ranged from fundraising to translations, from support in data gathering for estimates on homicides and disappearances in various regions of Colombia to editorial support to different Benetech-HRDAG ...
The interview poses questions about Lum's focus on artificial intelligence and its impact on predictive policing and sentencing programs.
I began working with HRDAG in the summer of 2001 before it was ever even called HRDAG. In fact, not intended as a boast, I think I’m responsible for coming up with the name. After contracting with Dr. Patrick Ball for a time writing the Analyzer data management platform, I left New York City and joined him in Washington, DC, at AAAS in 2002. Soon after starting, Patrick decided to establish an identity for this new team, consisting mainly of myself, Miguel Cruz and a handful of field relationships. We discussed what to name it briefly in the AAAS Science & Policy break room, which at the time, being in the mind of unclever descriptive naming ...
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," ...
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Patrick Ball. “A Definition of Database Design Standards for Human Rights Agencies.” © 1994 American Association for the Advancement of Science. [pdf]
HRDAG contributes to the project by helping to classify, filter, extract, and standardize the records so that they can be useful in the database.
Patrick Ball of the California-based Human Rights Data Analysis Group said he had calculated the mortality rate of political prisoners from 1985 to 1988 using reports completed by Habre’s feared secret police.
“If you’re not careful, you risk automating the exact same biases these programs are supposed to eliminate,” says Kristian Lum, the lead statistician at the San Francisco-based, non-profit Human Rights Data Analysis Group (HRDAG). Last year, Lum and a co-author showed that PredPol, a program for police departments that predicts hotspots where future crime might occur, could potentially get stuck in a feedback loop of over-policing majority black and brown neighbourhoods. The program was “learning” from previous crime reports. For Samuel Sinyangwe, a justice activist and policy researcher, this kind of approach is “especially nefarious” because police can say: “We’re not being biased, we’re just doing what the math tells us.” And the public perception might be that the algorithms are impartial.
