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From the article: Esta entidad, que existe desde 1991, es liderada por su fundador, Patrick Ball, un científico que acumula una experiencia de más de 25 años realizando análisis cuantitativos en los lugares y en las situaciones más convulsos del planeta. Sobre su colaboración con el proyecto del predictor de fosas clandestinas en México, único en el mundo, Ball afirmó en entrevista:
“Cuando hablamos de crímenes de lesa humanidad estamos hablando de instituciones, de organizaciones grandes, cometiendo miles o centenares de miles de violaciones a víctimas distribuidas sobre una geografía enorme. Para entender los patrones en esas violaciones, la estadística puede brindar una mirada sobre quiénes son los responsables materiales e intelectuales, quiénes son las víctimas y dónde o cuándo pasaron esas violaciones. Pero la estadística no es contabilidad, pues no estamos hablando solamente de las violaciones que podemos ver, sino que también debemos calcular las violaciones no observadas, las escondidas, invisibles, para incluir en nuestro análisis la totalidad de las violaciones”.
Kristian Lum, the lead statistician at the Human Rights Data Analysis Group, and an expert on algorithmic bias, said she hoped Stanford’s stumble made the institution think more deeply about representation.
“This type of oversight makes me worried that their stated commitment to the other important values and goals – like taking seriously creating AI to serve the ‘collective needs of humanity’ – is also empty PR spin and this will be nothing more than a vanity project for those attached to it,” she wrote in an email.
“In 2016, two researchers, the statistician Kristian Lum and the political scientist William Isaac, set out to measure the bias in predictive policing algorithms. They chose as their example a program called PredPol. … Lum and Isaac faced a conundrum: if official data on crimes is biased, how can you test a crime prediction model? To solve this technique, they turned to a technique used in statistics and machine learning called the synthetic population.”
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,” Ms. Pillay said in a statement that accompanied the report, which observed that many killings in Syria were undocumented.
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.
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Q3. What are the steps in an MSE analysis?
Q4. What does data collection look like in the human rights context? What kind of data do you collect?
Q5. [In depth] Do you include unnamed or anonymous victims in the matching process?
Q6. What do you mean by "cleaning" and "canonicalization?"
Q7. [In depth] What are some of the challenges of canonicalization? (more…)
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.
Who We Are
The Human Rights Data Analysis Group is a non-profit, non-partisan organization that applies rigorous science to the analysis of human rights violations around the world. We are a team with expertise in mathematical statistics, computer science, demography, and social science. 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 ...
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 ...
In this
Granta article, HRDAG explains that neither the infectiousness nor the deadliness of the disease is set in stone.
The summer of 2002 in Washington, DC, was steamy and hot, which is how I remember my introduction to HRDAG. I had begun working with them, while they were still at AAAS, in the late spring, learning all about their core concepts: duplicate reporting and MSE, controlled vocabularies, inter-rater reliability, data models and more. The days were long, with a second shift more often than not running late into the evening. In addition to all the learning, I also helped with matching for the Chad project – that is, identifying multiple records of the same violation – back when matching was done by hand. But it was not long after I arrived in Washington ...
I joined the Benetech Human Rights Program at essentially the same time that HRDAG did, coming to Benetech from years of analyzing data for large companies in the transportation, hospitality and retail industries. But the data that HRDAG dealt with was not like the data I was familiar with, and I was fascinated to learn about how they used the data to determine "who did what to whom." Although some of the methodologies were similar to what I had experience with in the for-profit sector, the goals and beneficiaries of the analyses were very different.
At Benetech, I was initially predominantly focused on product management for Martus, a free ...
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 ...
The interview poses questions about Lum's focus on artificial intelligence and its impact on predictive policing and sentencing programs.
Margot is a professor in the Department of Energy Resources Engineering at Stanford University, interested in computer simulation and mathematical analysis of engineering processes.
I began working with HRDAG in the summer of 2001 before it was ever even called HRDAG. In fact, not intended as a boast, I think I’m responsible for coming up with the name. After contracting with Dr. Patrick Ball for a time writing the Analyzer data management platform, I left New York City and joined him in Washington, DC, at AAAS in 2002. Soon after starting, Patrick decided to establish an identity for this new team, consisting mainly of myself, Miguel Cruz and a handful of field relationships. We discussed what to name it briefly in the AAAS Science & Policy break room, which at the time, being in the mind of unclever descriptive naming ...
Kristian Lum, lead statistician at HRDAG | Predictive Policing: Bias In, Bias Out | 56 mins
In Orleans Parish, Louisiana, home of New Orleans, 78 percent of wrongful convictions have been linked to a police officer’s failure to share exculpatory evidence with the defense. This is a rate more than double the national average.But while these actions, or any misconduct, by law enforcement personnel may be recorded officially, the data may be difficult to use or find. Depending on a parish’s resources, the data may be archived in a non-digital format, for example, on paper.
Innocence Project New Orleans (IPNO) has as its mission the overturning of wrongful convictions in Louisiana. A police officer involved in a wrongful conviction may ...
