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
As a woman, mother and sociologist who is curious about the patterns of our political past in Guatemala, I feel privileged to know and work with the HRDAG team. Collaborating and learning from people like Patrick, Megan, Suzanne, Beatriz and Tamy has been an invaluable gift. I have discovered many things, both human and academic. For example, I’ve learned new ways of seeing what seemed everyday and simple, to discover that not only do the social sciences and statistics work hand in hand, but that they are critical for understanding Guatemala’s reality.
Twenty years ago, on 29 December, 1996, Guatemala made history by signing the Guatemala Peace ...
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 interview poses questions about Lum's focus on artificial intelligence and its impact on predictive policing and sentencing programs.
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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Kristian Lum, lead statistician at HRDAG | Predictive Policing: Bias In, Bias Out | 56 mins
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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. ...
“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.
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.
Megan Price (2017). Estimating the human toll in Syria. Nature. 8 February 2017. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. Nature Human Behaviour. ISSN 2397-3374.
The University of Michigan College of Literature, Science and the Arts profiled Patrick Ball in its fall 2016 issue of the alumni magazine. Here’s an excerpt:
Ball believes doing this laborious, difficult work makes the world a more just place because it leads to accountability.
“My part is a specific, narrow piece, which just happens to fit with the skills I have,” he says. “I don’t think that what we do is in any way the best or most important part of human rights activism. Sometimes, we are just a footnote—but we are a really good footnote.”
