714 results for search: %E3%80%8E%EB%8F%84%EB%B4%89%EA%B5%AC%EC%83%81%ED%99%A9%EA%B7%B9%E3%80%8F%20O6O%E3%85%A15O1%E3%85%A19997%20%EC%82%AC%EC%8B%AD%EB%8C%80%EB%8C%80%ED%99%94%EC%96%B4%ED%94%8C%20%EC%BB%A4%ED%94%8C%EC%BB%A4%EB%AE%A4%EB%8B%88%ED%8B%B0%E2%86%95%EB%AF%B8%EC%8A%A4%EB%85%80%EB%8D%B0%EC%9D%B4%ED%8C%85%E2%92%AE%EB%B0%A9%EC%95%84%EC%83%81%ED%99%A9%EA%B7%B9%20%E3%83%8D%E5%AF%9D%20bifoliate/feed/content/colombia/Co-union-violence-paper-response.pdf

Different Convenience Samples, Different Stories: The Case of Sierra Leone.

Anita Gohdes. “Different Convenience Samples, Different Stories: The Case of Sierra Leone.” Benetech. 2010. © 2010 Benetech. Creative Commons BY-NC-SA.

On ensuring a higher level of data quality when documenting human rights violations to support research into the origins and cause of human rights violations

Romesh Silva. “On ensuring a higher level of data quality when documenting human rights violations to support research into the origins and cause of human rights violations.” ASA Proceedings of the Joint Statistical Meetings, the International Biometric Society (ENAR and WNAR), the Institute of Mathematical Statistics, and the Statistical Society of Canada. August, 2002.

Making the Case. Investigating Large Scale Human Rights Violations Using Information Systems and Data Analysis

Patrick Ball, Herbert F. Spirer, and Louise Spirer, eds. Making the Case. Investigating Large Scale Human Rights Violations Using Information Systems and Data Analysis . © 2000 American Association for the Advancement of Science. All rights reserved. Reprinted with permission. [full text] [intro] [chapters 1 2 3 4 5 67 8 9 10 11 12]

Using Machine Learning to Help Human Rights Investigators Sift Massive Datasets

How we built a model to search hundreds of thousands of text messages from the perpetrators of a human rights crime.

Where Stats and Rights Thrive Together

Everyone I had the pleasure of interacting with enriched my summer in some way.

War and Illness Could Kill 85,000 Gazans in 6 Months

HRDAG director of research Patrick Ball is quoted in this New York Times article about a paper that models death tolls in Gaza.

Using Math and Science to Count Killings in Syria

In this afternoon "Lightning Talk" at RightsCon 2014, Megan Price spoke about the importance of using models to adjust for variability when reporting human rights violations and mentioned innovative tools that can be used for tracking abuses. RIGHTSCON March 4, 2014 San Francisco, California Link to RightsCon program Back to Talks

Big Data Predictive Analytics Comes to Academic and Nonprofit Institutions to Fuel Innovation

"Revolution Analytics will allow HRDAG to handle bigger data sets and leverage the power of R to accomplish this goal and uncover the truth." Director of Research Megan Price is quoted. REVOLUTION ANALYTICS Press release February 4, 2014 Link to press release Back to Press Room

Palantir Has Secretly Been Using New Orleans to Test Its Predictive Policing Technology

One of the researchers, a Michigan State PhD candidate named William Isaac, had not previously heard of New Orleans’ partnership with Palantir, but he recognized the data-mapping model at the heart of the program. “I think the data they’re using, there are serious questions about its predictive power. We’ve seen very little about its ability to forecast violent crime,” Isaac said.

Celebrating Women in Statistics

kristian lum headshot 2018In her work on statistical issues in criminal justice, Lum has studied uses of predictive policing—machine learning models to predict who will commit future crime or where it will occur. In her work, she has demonstrated that if the training data encodes historical patterns of racially disparate enforcement, predictions from software trained with this data will reinforce and—in some cases—amplify this bias. She also currently works on statistical issues related to criminal “risk assessment” models used to inform judicial decision-making. As part of this thread, she has developed statistical methods for removing sensitive information from training data, guaranteeing “fair” predictions with respect to sensitive variables such as race and gender. Lum is active in the fairness, accountability, and transparency (FAT) community and serves on the steering committee of FAT, a conference that brings together researchers and practitioners interested in fairness, accountability, and transparency in socio-technical systems.

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, ...

UN Raises Estimate of Dead in Syrian Conflict to 191,000

Nick Cumming-Bruce of the New York Times writes about 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: In its third report on Syria commissioned by the United Nations, the Human Rights Data Analysis Group identified 191,369 deaths from the start of the conflict in March 2011 to April 2014, more than double the 92,901 deaths cited in their last report, which covered the first two years of the conflict. “Tragically, it is probably an underestimate of the real total number of people killed during the first three years of this murderous conflict,” ...

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 ...

Emeritus Advisers

We are grateful to our past advisers for their contributions to HRDAG. Advisory Board Audrey Chapman, Healey Chair in Medical Humanities and Bioethics, University of Connecticut 2013-2014 (one-year term)

HRDAG Offers New R Package – dga

Much of the work we do at HRDAG involves estimating the number of undocumented deaths using a statistical technique called multiple systems estimation (MSE, described in more detail here). One of our goals is to make this class of methods more broadly available to human rights researchers. In particular, we are finding that Bayesian approaches are extremely valuable for MSE. Accordingly, we are pleased to offer a new R package called dga (“decomposable graphs approach”) that performs Bayesian model averaging for MSE. The main function in this package implements a model created by David Madigan and Jeremy York. This model was designed to ...

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," ...

The Death Toll in Syria

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.

“El reto de la estadística es encontrar lo escondido”: experto en manejo de datos sobre el conflicto

In this interview with Colombian newspaper El Espectador, Patrick Ball is quoted as saying “la gente que no conoce de álgebra nunca debería hacer estadísticas” (people who don’t know algebra should never do statistics).

Our work has been used by truth commissions, international criminal tribunals, and non-governmental human rights organizations. We have worked with partners on projects on five continents.

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