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Middle East

Syria

At Toronto’s Tamil Fest, human rights group seeks data on Sri Lanka’s civil war casualties

Earlier this year, the Canadian Tamil Congress connected with HRDAG to bring its campaign to Toronto’s annual Tamil Fest, one of the largest gatherings of Canada’s Sri Lankan diaspora.

Ravichandradeva, along with a few other volunteers, spent the weekend speaking with festival-goers in Scarborough about the project and encouraging them to come forward with information about deceased or missing loved ones and friends.

“The idea is to collect thorough, scientifically rigorous numbers on the total casualties in the war and present them as a non-partisan, independent organization,” said Michelle Dukich, a data consultant with HRDAG.

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 ...

Clustering and Solving the Right Problem

In our database deduplication work, we’re trying to figure out which records refer to the same person, and which other records refer to different people. We write software that looks at tens of millions of pairs of records. We calculate a model that assigns each pair of records a probability that the pair of records refers to the same person. This step is called pairwise classification. However, there may be more than just one pair of records that refer to the same person. Sometimes three, four, or more reports of the same death are recorded. So once we have all the pairs classified, we need to decide which groups of records refer to the ...

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.

Kristian Lum in Bloomberg

The interview poses questions about Lum's focus on artificial intelligence and its impact on predictive policing and sentencing programs.

HRDAG Names New Board Member Margot Gerritsen

Margot is a professor in the Department of Energy Resources Engineering at Stanford University, interested in computer simulation and mathematical analysis of engineering processes.

Featured Video

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

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To speak with the researchers at HRDAG, please fill out the form below. You can search our Press Room by keyword or by year.

Publications

From time to time, we issue our own scientific reports that focus on the statistical aspects of the data analysis we have done in support of our partners. These reports are non-partisan, and they leave the work of advocacy to our partners. You can search our publications by keyword or by year.

The Atrocity Archives

Courts and police departments are turning to AI to reduce bias, but some argue it’ll make the problem worse

Kristian Lum: “The historical over-policing of minority communities has led to a disproportionate number of crimes being recorded by the police in those locations. Historical over-policing is then passed through the algorithm to justify the over-policing of those communities.”

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.

¿Quién le hizo qué a quién? Planear e implementar un proyecto a gran escala de información en derechos humanos.

Patrick Ball (2008). “¿Quién le hizo qué a quién? Planear e implementar un proyecto a gran escala de información en derechos humanos.” (originally in English at AAAS) Translated by Beatriz Verjerano. Palo Alto, California: Benetech.

Foundation of Human Rights Statistics in Sierra Leone

Richard Conibere (2004). Foundation of Human Rights Statistics in Sierra Leone (abstr.), Joint Statistical Meetings. Toronto, Canada.

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.

Predictive policing violates more than it protects

William Isaac and Kristian Lum. Predictive policing violates more than it protects. USA Today. December 2, 2016. © USA Today.

William Isaac and Kristian Lum. Predictive policing violates more than it protects. USA Today. December 2, 2016. © USA Today.

Indirect Sampling to Measure Conflict Violence: Trade-offs in the Pursuit of Data That Are Good, Cheap, and Fast

Romesh Silva and Megan Price. “Indirect Sampling to Measure Conflict Violence: Trade-offs in the Pursuit of Data That Are Good, Cheap, and Fast.” Journal of the American Medical Association. 306(5):547-548. 2011. © 2011 JAMA. All rights reserved.

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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