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Shemika Lamare has joined the HRDAG team as our new data science fellow.
We're thrilled to announce that Tarak Shah has joined our team as our new data scientist.
On September 7, 2018, Kristian Lum and Patrick Ball participated in a panel at Disrupt San Francisco by TechCrunch. The talk was titled "Dismantling Algorithmic Bias." Brian Brackeen of Kairos was part of the panel as well, and the talk was moderated by TechCrunch reporter Megan Rose Dickey.
From the TechCrunch website, "Disrupt is a 3-day conference focused on breaking technology news and developments with big-name thought leaders who are making waves in the industry."
Video of the talk is available here, and Megan Rose Dickey's coverage is here.
The interview poses questions about Lum's focus on artificial intelligence and its impact on predictive policing and sentencing programs.
Illuminating Data's Dark Side: Big data create conveniences, but we must consider who designs these tools, who benefits from them, and who is left out of the equation.
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.
The first time I met anyone at HRDAG, I was a journalist. It was 2006. I was working on a story about a graduate student at Carnegie Mellon who’d collaborated with the organization on a survey in Sierra Leone, and I contacted Patrick Ball to discuss the work. At the time, I found him challenging.
But I thought his work—trying to estimate how many people were killed, or, in that study, otherwise injured, during wars—was fascinating. Over the next few years, I got to know other researchers working on similar questions. In 2008, as the war in Iraq ramped up, I spoke with epidemiologists from Johns Hopkins University, the World Health Organiz...
For more than 10 years, and with regularity, Mexican authorities have been discovering mass graves, known as fosas clandestinas, in which hundreds of bodies and piles of bones have been found. The casualties are attributed broadly to the country’s “drug war,” although the motivations and perpetrators behind the mass murders are often unknown.
Recently, HRDAG collaborated with two partners in Mexico—Data Cívica and Programa de Derechos Humanos of the Universidad Iberoamericana—to model the probability of identifying a hidden grave in each county (municipio). The model uses an set of independent variables and data about graves from 2013 ...
Statisticien, Patrick Ball est à la barre ce vendredi matin. L’expert est entendu sur le taux de mortalité dans les centres de détention au Tchad sous Habré. Désigné par la chambre d’accusation, il dira avoir axé ses travaux sur des témoignages, des données venant des victimes et des documents de la DDS (Direction de la Documentation et de la Sécurité).
Next week, on June 11, Oxford University Press officially puts Counting Civilian Casualties: An Introduction to Recording and Estimating Nonmilitary Deaths in Conflict on the market. This textbook, edited by Taylor B. Seybolt, Jay D. Aronson, and Baruch Fischhoff, responds to the increasing concern for civilians in conflict and aims to promote scientific dialogue by highlighting the strengths and weaknesses of the most commonly used casualty recording and estimation techniques.
HRDAG is very well represented here, as our colleagues have co-authored four chapters, and Nicholas Jewell, who sits on our Science Committee, has co-authored a fifth. ...
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Help HRDAG use data science to work for justice, accountability, and human rights. We are nonpartisan and nonprofit, but we are not neutral; we are always on the side of human rights.
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We are a team of experts in machine learning, applied and mathematical statistics, computer science, demography, and social science, and ...
The Rafto Foundation, an international human rights organization, has bestowed the 2021 Rafto Prize to HRDAG for its distinguished work defending human rights and democracy.
“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.
In 1995, the Haitian National Commission for Truth and Justice (CNVJ) requested the advice of the American Association for the Advancement of Science (AAAS) and Dr. Patrick Ball on how to develop a large-scale project to take the testimonies of several thousand witnesses of human rights abuses in Haiti.
The team conducted work incorporating over 5,000 interviews covering over 8,500 victims to produce detailed regional analyses, using quantitative material from the interviews, historical, economic and demographic analysis.
Propelled by the impact of data analysis in El Salvador, Patrick Ball applied his WDWTW model to human rights information in other countries. Throughout the 1990’s, Ball worked at the American Association for the Advancement of Science (AAAS) analyzing large-scale human rights violations in Ethiopia, South Africa, Haiti and Guatemala. Together with senior scientific colleagues, including statistician Dr. Herb Spirer, Ball developed new methods for analyzing state-sanctioned violence. This chapter documents how the research expanded when a group of nongovernmental organizations in Guatemala asked the scientific community to gather and analyze ...
At HRDAG, 2021 was all about service and partnership.
“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.”
Laurel Eckhouse (2017). Big data may be reinforcing racial bias in the criminal justice system. Washington Post. 10 February 2017. © 2017 Washington Post.
Laurel Eckhouse (2017). Big data may be reinforcing racial bias in the criminal justice system. Washington Post. 10 February 2017. © 2017 Washington Post.
