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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 ...
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 ...
Margot is a professor in the Department of Energy Resources Engineering at Stanford University, interested in computer simulation and mathematical analysis of engineering processes.
The summer of 2002 in Washington, DC, was steamy and hot, which is how I remember my introduction to HRDAG. I had begun working with them, while they were still at AAAS, in the late spring, learning all about their core concepts: duplicate reporting and MSE, controlled vocabularies, inter-rater reliability, data models and more. The days were long, with a second shift more often than not running late into the evening. In addition to all the learning, I also helped with matching for the Chad project – that is, identifying multiple records of the same violation – back when matching was done by hand. But it was not long after I arrived in Washington ...
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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Next week, on June 11, Oxford University Press officially puts Counting Civilian Casualties: An Introduction to Recording and Estimating Nonmilitary Deaths in Conflict on the market. This textbook, edited by Taylor B. Seybolt, Jay D. Aronson, and Baruch Fischhoff, responds to the increasing concern for civilians in conflict and aims to promote scientific dialogue by highlighting the strengths and weaknesses of the most commonly used casualty recording and estimation techniques.
HRDAG is very well represented here, as our colleagues have co-authored four chapters, and Nicholas Jewell, who sits on our Science Committee, has co-authored a fifth. ...
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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.
“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.”
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.
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.
