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UN Raises Estimate of Dead in Syrian Conflict to 191,000
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.
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Political Killings in Kosovo, March–June, 1999
Political Killings in Kosovo, March–June, 1999. American Association for the Advancement of Science, Science and Human Rights Program. © 2000 American Bar Association Central and East European Law Initiative.
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.
Una Mirada al Archivo Histórico de la Policia Nacional a Partir de un Estudio Cuantitativo
Carolina López, Beatriz Vejarano, and Megan Price. 2016. Human Rights Data Analysis Group. © 2016 HRDAG.Creative Commons BY-NC-SA.
Evaluation of the Database of the Kosovo Memory Book
Jule Krüger and Patrick Ball (2014). An analysis accompanying the release of the Kosovo Memory Book. December 10, 2014. © 2014 HRDAG. Creative Commons BY-NC-SA.
Big Data, Selection Bias, and the Statistical Patterns of Mortality in Conflict
Megan Price and Patrick Ball (2014). SAIS Review of International Affairs © 2014 The Johns Hopkins University Press. This article first appeared in SAIS Review, Volume 34, Issue 1, Winter-Spring 2014, pages 9-20. All rights reserved.
Perú
Gaza death toll 40% higher than official number, Lancet study finds
“Patrick Ball, a statistician at the US-based Human Rights Data Analysis Group not involved in the research, has used capture-recapture methods to estimate death tolls for conflicts in Guatemala, Kosovo, Peru and Colombia.
Ball told AFP the well-tested technique had been used for centuries and that the researchers had reached “a good estimate” for Gaza.”
Data ‘hashing’ improves estimate of the number of victims in databases
But while HRDAG’s estimate relied on the painstaking efforts of human workers to carefully weed out potential duplicate records, hashing with statistical estimation proved to be faster, easier and less expensive. The researchers said hashing also had the important advantage of a sharp confidence interval: The range of error is plus or minus 1,772, or less than 1 percent of the total number of victims.
“The big win from this method is that we can quickly calculate the probable number of unique elements in a dataset with many duplicates,” said Patrick Ball, HRDAG’s director of research. “We can do a lot with this estimate.”
Using Math and Science to Count Killings in Syria
Can We Harness AI To Fulfill The Promise Of Universal Human Rights?
The Human Rights Data Analysis Group employs AI to analyze data from conflict zones, identifying patterns of human rights abuses that might be overlooked. This assists international organizations in holding perpetrators accountable.
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Celebrating Women in Statistics
In 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.