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Who Did What to Whom? Planning and Implementing a Large Scale Human Rights Data Project

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Patrick Ball. Who Did What to Whom? Planning and Implementing a Large Scale Human Rights Data Project. © 1996 American Association for the Advancement of Science.


How Structuring Data Unburies Critical Louisiana Police Misconduct Data

In Louisiana, appeals for police disciplinary action are often buried in meeting minutes. HRDAG uses machine learning to extract, structure data and make it searchable..

Data Collection and Documentation for Truth-Seeking and Accountability

Megan Price and Patrick Ball (2014). The Syrian Justice and Accountability Centre. © 2014 SJAC.Creative Commons BY-NC-SA.


Using statistics to estimate the true scope of the secret killings at the end of the Sri Lankan civil war

In the last three days of the Sri Lankan civil war, as thousands of people surrendered to government authorities, hundreds of people were put on buses driven by Army officers. Many were never seen again.

In a report released today (see here), the International Truth and Justice Project for Sri Lanka and the Human Rights Data Analysis Group showed that over 500 people were disappeared on only three days — 17, 18, and 19 May.


Former Leader of Guatemala Is Guilty of Genocide Against Mayan Group


Counting the Dead in Syria


In Syrian Conflict, Real-Time Evidence Of Violations


60,000 Dead in Syria? Why the Death Toll is Likely Even Higher


Benetech Celebrates Milestone; Human Rights Data Analysis Group Transitioning into Independent Organization


Syrian Death Toll Reaches 60,000, Says UN Rights Agency


Data Dive Reveals 15,000 New Victims of Syria War


The Body Counter


Calculating US police killings using methodologies from war-crimes trials

100x100-boingboing-logoCory Doctorow of Boing Boing writes about HRDAG director of research Patrick Ball’s article “Violence in Blue,” published March 4 in Granta. From the post: “In a must-read article in Granta, Ball explains the fundamentals of statistical estimation, and then applies these techniques to US police killings, merging data-sets from the police and the press to arrive at an estimate of the knowable US police homicides (about 1,250/year) and the true total (about 1,500/year). That means that of all the killings by strangers in the USA, one third are committed by the police.”


Carnegie Mellon Partners With Human Rights Data Analysis Group To Improve Syrian Casualty Reporting


Megan Price: Life-Long ‘Math Nerd’ Finds Career in Social Justice

“I was always a math nerd. My mother has a polaroid of me in the fourth grade with my science fair project … . It was the history of mathematics. In college, I was a math major for a year and then switched to statistics.

I always wanted to work in social justice. I was raised by hippies, went to protests when I was young. I always felt I had an obligation to make the world a little bit better.”


Death and the Mainframe: How data analysis can help document human rights atrocities


Estimating Deaths


Data Mining on the Side of the Angels

“Data, by itself, isn’t truth.” How HRDAG uses data analysis and statistical methods to shed light on mass human rights abuses. Executive director Patrick Ball is quoted from his speech at the Chaos Communication Congress in Hamburg, Germany.


Death March

A mapped representation of the scale and spread of killings in Syria. HRDAG’s director of research, Megan Price, is quoted.


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.


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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