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Liberian Truth and Reconciliation Commission Data

In July 2009, The Human Rights Data Analysis Group concluded a three-year project with the Liberian Truth and Reconciliation Commission to help clarify Liberia’s violent history and hold perpetrators of human rights abuses accountable for their actions. In the course of this work, HRDAG analyzed more than 17,000 victim and witness statements collected by the Liberian Truth and Reconciliation Commission and compiled the data into a report entitled “Descriptive Statistics From Statements to the Liberian Truth and Reconciliation Commission.” Liberian TRC data and the accompanying data dictionary anonymized-statgivers.csv contains information ...

Data Mining for Good: CJA Drink + Think

At the Center for Justice and Accountability's happy hour, "Drink and Think," Patrick Ball spoke about "Data Mining for Good." The talk included a discussion of how HRDAG brings human rights abusers to justice through data analysis, and HRDAG's work conducting quantitative analysis for truth commissions, NGOs, the UN and other partners. The event was held at Eventbrite. More photos are below. The Center for Justice and Accountability Young Professionals' Committee for Human Rights September 16, 2014 San Francisco, California Link to CJA event page Back to Talks   All photos © 2014 Carter Brooks.

Uncertainty in COVID Fatality Rates

In this Granta article, HRDAG explains that neither the infectiousness nor the deadliness of the disease is set in stone.

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 ...

Karl E. Peace Award Recognizes Work of Patrick Ball

The American Statistical Association’s 2018 Karl E. Peace Award for Outstanding Statistical Contributions for the Betterment of Society recently recognized the work of leading human rights mathematician Patrick Ball of the Human Rights Data Analysis Group (HRDAG). The award is presented annually to statisticians whose exemplary statistical research is matched by the impact their work has had on the lives of people.

Established by the family of Karl E. Peace in honor of his work for the good of society, the award—announced at the Joint Statistical Meetings—is bestowed upon distinguished individual(s) who have made substantial contributions to the statistical profession, contributions that have led in direct ways to improving the human condition. Recipients will have demonstrated through their accomplishments their commitment to service for the greater good.”

This year, Ball became the 10th recipient of the award. Read more …


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 ...

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 ...

About HRDAG

We are non-partisan—we do not take sides in political or military conflicts, nor do we advocate any particular political party or government policy. However, we are not neutral: we are always in favor of human rights. We support the protections established in the Universal Declaration of Human Rights, the International Covenant on Civil and Political Rights, and other international human rights treaties and instruments.

New death toll estimated in Syrian civil war

Kevin Uhrmacher of the Washington Post prepared a graph that illustrates reported deaths over time, by number of organizations reporting the deaths. Washington Post Kevin Uhrmacher August 22, 2014 Link to story on Washington Post Related blogpost (Updated Casualty Count for Syria) Back to Press Room  

Team Page


Why It Took So Long To Update the U.N.-Sponsored Syria Death Count

In this story, Carl Bialik of FiveThirtyEight interviews HRDAG executive director Patrick Ball about the process of de-duplication, integration of databases, and machine-learning in the recent enumeration of reported casualties in Syria. New reports of old deaths come in all the time, Ball said, making it tough to maintain a database. The duplicate-removal process means “it’s a lot like redoing the whole project each time,” he said. FiveThirtyEight Carl Bialik August 23, 2014 Link to story on FiveThirtyEight Related blogpost (Updated Casualty Count for Syria) Back to Press Room  

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Featured Video

Kristian Lum, lead statistician at HRDAG | Predictive Policing: Bias In, Bias Out | 56 mins

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.


Hunting for Mexico’s mass graves with machine learning

“The model uses obvious predictor variables, Ball says, such as whether or not a drug lab has been busted in that county, or if the county borders the United States, or the ocean, but also includes less-obvious predictor variables such as the percentage of the county that is mountainous, the presence of highways, and the academic results of primary and secondary school students in the county.”


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

/whodidwhattowhom/contents.html

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.


Violence in Blue

Patrick Ball. 2016. Granta 134: 4 March 2016. © Granta Publications. All rights reserved.


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


Weapons of Math Destruction

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.


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.


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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