706 results for search: %EB%82%A8%EA%B5%AC%ED%9C%B4%EA%B2%8C%ED%85%94%E3%81%ACjusobot%E3%80%81%EF%BD%830m%E2%99%99%EB%82%A8%EA%B5%AC%EA%B1%B4%EB%A7%88%E2%99%A9%EB%82%A8%EA%B5%AC%EC%97%85%EC%86%8C%E2%9C%88%EB%82%A8%EA%B5%AC%EB%A6%BD%EB%B0%A9%20%EB%82%A8%EA%B5%AC%EB%8B%AC%EB%A6%BC/feed/content/colombia/copyright
Carolina López, Beatriz Vejarano, and Megan Price. 2016. Human Rights Data Analysis Group. © 2016 HRDAG.Creative Commons BY-NC-SA.
Megan Price and Patrick Ball (2014). SAIS Review of International Affairs © 2014 The Johns Hopkins University Press. This article first appeared in SAIS Review, Volume 34, Issue 1, Winter-Spring 2014, pages 9-20. All rights reserved.
Anita Gohdes and Megan Price (2013). Journal of Conflict Resolution, Volume 57 Issue 6 December 2013. © 2013 Journal of Conflict Resolution. All rights reserved. Reprinted with permission of SAGE. [online abstract]DOI: 10.1177/0022002712459708.
Structural Zero Issue 03
August 24, 2025
Part Three of Our Three-Part “Gathering the Data” Series. Read part one and part two.
In computer security, “security” is always relative to something. What are we actually defending against, and how are we doing it? This is our “threat model.”
My colleagues and I have been using scientific tools to analyze evidence of human rights abuses, including using statistics to uncover mass graves in Mexico and analyzing under-reported police homicides in the United States.
Our work isn’t always popular. It can infuriate those in power who want to cover up incriminating truths about the ...
"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
As we get ready to begin our fourth year as an independent nonprofit, we are, as always, indebted to our Advisory Board and to our funders for their support and vision. We’re finishing up a busy year that took us to Dakar (for the trial of former Chadian dictator Hissène Habré), Pristina (for the release of the Kosovo Memory Book), Colombia (for work on a book about the Guatemalan Police Archives), and kept us busy here at home working on police violence statistics. But one of our biggest victories has been to score a new, talented, wise Advisory Board member—Michael Bear Kleinman, whom we first met when he was working with Humanity United.
...
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.
In 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.
Today Amnesty International released “‘It breaks the human’: Torture, disease and death in Syria’s prisons ,” a report detailing the conditions and mortality in Syrian prisons from 2011 to 2015, including data analysis conducted by HRDAG.
The report provides harrowing accounts of ill treatment of detainees in Syrian prisons since the conflict erupted in March 2011, and publishes HRDAG’s estimate of the number of killings that occurred inside the prisons.
To accompany the report, HRDAG has released a technical memo that explains the methodology, sources, and implications of the findings. The HRDAG team used data from four ...
On November 26, HRDAG colleague Anita Gohdes was awarded the German Dissertation Prize for the Social Sciences. The patron of the prize is the President of the German Parliament, Norbert Lammert, who presented Anita with the award.
Anita’s dissertation, “Repression 2.0: The Internet in the War Arsenal of Modern Dictators,” investigates the role played by social media networks in modern dictatorships, such as President Assad’s regime in Syria. On one hand, Anita argues, social media can help opposition groups to organize more effectively, but on the other hand, the same networks allow regimes to monitor and manipulate the population. ...
ed. by Taylor B. Seybolt, Jay D. Aronson, and Baruch Fischhoff. Oxford University Press. © 2013 Oxford University Press. All rights reserved.
The following four chapters are included:
— Todd Landman and Anita Gohdes (2013). “A Matter of Convenience: Challenges of Non-Random Data in Analyzing Human Rights Violations in Peru and Sierra Leone.”
— Jeff Klingner and Romesh Silva (2013). “Combining Found Data and Surveys to Measure Conflict Mortality.”
— Daniel Manrique-Vallier, Megan E. Price, and Anita Gohdes (2013). “Multiple-Systems Estimation Techniques for Estimating Casualties in Armed Conflict.”
— Jule Krüger, Patrick Ball, Megan Price, and Amelia Hoover Green (2013). “It Doesn’t Add Up: Methodological and Policy Implications of Conflicting Casualty Data.”
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.
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 ...
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 ...
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 ...
Margot is a professor in the Department of Energy Resources Engineering at Stanford University, interested in computer simulation and mathematical analysis of engineering processes.
