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Beautiful game, ugly truth?
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
The Allegheny Family Screening Tool’s Overestimation of Utility and Risk
Anjana Samant, Noam Shemtov, Kath Xu, Sophie Beiers, Marissa Gerchick, Ana Gutierrez, Aaron Horowitz, Tobi Jegede, Tarak Shah (2023). The Allegheny Family Screening Tool’s Overestimation of Utility and Risk. Logic(s). 13 December, 2023. Issue 20.
Anjana Samant, Noam Shemtov, Kath Xu, Sophie Beiers, Marissa Gerchick, Ana Gutierrez, Aaron Horowitz, Tobi Jegede, Tarak Shah (2023). The Allegheny Family Screening Tool’s Overestimation of Utility and Risk. Logic(s). 13 December, 2023. Issue 20.
Middle East
Syria
HRDAG – 25 Years and Counting
Today is a very special day for all of us at HRDAG. This is, of course, the 68th anniversary of the Universal Declaration of Human Rights—but this day also marks our 25th year of using statistical science to support the advancement of human rights.
It started 25 years ago, in December 1991, in San Salvador, when Patrick Ball was invited to work with the Salvadoran Lutheran Church to design a database to keep track of human rights abuses committed by the military in El Salvador. That work soon migrated to the NGO Human Rights Commission (CDHES). Fueled by thin beer and pupusas, Patrick dove into the deep world of data from human rights testimonies, ...
Where Stats and Rights Thrive Together
Everyone I had the pleasure of interacting with enriched my summer in some way.
Mexico
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 ...
Reflections: Growing and Learning in Guatemala
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 ...
Kristian Lum in Bloomberg
The interview poses questions about Lum's focus on artificial intelligence and its impact on predictive policing and sentencing programs.
Update of Iraq and Syria Data in New Paper
This week The Statistical Journal of the IAOS published a new(ish) paper by Megan Price and Patrick Ball. The open-access paper, Selection bias and the statistical patterns of mortality in conflict, is a revisiting and updating of both the Iraq and Syria examples used in an earlier paper, Big Data, Selection Bias, and the Statistical Patterns of Mortality in Conflict, which was published last year inThe SAIS Review of International Affairs (JHU Press, 2014).
HRDAG believes that the concerns highlighted by these examples are important for a wide variety of audiences, including both the foreign policy readers reached by The SAIS Review and the ...
HRDAG Names New Board Member Margot Gerritsen
Margot is a professor in the Department of Energy Resources Engineering at Stanford University, interested in computer simulation and mathematical analysis of engineering processes.
Nonprofits Are Taking a Wide-Eyed Look at What Data Could Do
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," ...
Featured Video
Kristian Lum, lead statistician at HRDAG | Predictive Policing: Bias In, Bias Out | 56 mins
The Limits of Observation for Understanding Mass Violence.
Megan Price and Patrick Ball. 2015. Canadian Journal of Law and Society / Revue Canadienne Droit et Société volume 30 issue 2 (June): 1-21. doi:10.1017/cls.2015.24. © Cambridge University Press. All rights reserved. Restricted access.
Syrian civil war death toll exceeds 190,000, U.N. reports
Ayan Sheikh of PBS News Hour reports on the UN Office of the High Commissioner of Human Right’s release of HRDAG’s third report on reported killings in the Syrian conflict.
From the article:
The latest death toll figure covers the period from March 2011 to April of this year, came from the Human Rights Data Analysis Group and is the third study of its kind on Syria. The analysis group identified 191,269 deaths. Data was collected from five different sources to exclude inaccuracies and repetitions.
PBS News Hour
Ayan Sheikh
August 22, 2014
Link to story on PBS News Hour
Related blogpost (Updated Casualty Count for Syria)
Back to Press Room
Rise of the racist robots – how AI is learning all our worst impulses
“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.
Haiti
In 1995, the Haitian National Commission for Truth and Justice (CNVJ) requested the advice of the American Association for the Advancement of Science (AAAS) and Dr. Patrick Ball on how to develop a large-scale project to take the testimonies of several thousand witnesses of human rights abuses in Haiti.
The team conducted work incorporating over 5,000 interviews covering over 8,500 victims to produce detailed regional analyses, using quantitative material from the interviews, historical, economic and demographic analysis.
Welcoming our new Technical Lead
After almost two months of searching for the perfect fit, we’re very pleased to announce that Josh Shadlen has joined HRDAG as our new technical lead. Finding Josh was no easy feat. We were looking for what many people would call a “data scientist,” that is, someone with expertise in both computer science and statistics. These days, “data science” is one of the hottest fields out there.
Bringing the perfect mix of academic depth and thoughtful reflection, Josh stood out for us. With prior jobs including gigs at Silicon Valley startups and Twitter, he’s got high-level (more…)
The Data Scientist Helping to Create Ethical Robots
Kristian Lum is focusing on artificial intelligence and the controversial use of predictive policing and sentencing programs.
What’s the relationship between statistics and AI and machine learning?
AI seems to be a sort of catchall for predictive modeling and computer modeling. There was this great tweet that said something like, “It’s AI when you’re trying to raise money, ML when you’re trying to hire developers, and statistics when you’re actually doing it.” I thought that was pretty accurate.