Patrick Ball. 2015. The Bigness of Big Data: samples, models, and the facts we might find when looking at data. In The Transformation of Human Rights Fact-Finding, ed. Philip Alston and Sarah Knuckey. New York: Oxford University Press. ISBN: 9780190239497. © The Oxford University Press. All rights reserved.
We’re pleased to announce that Camille Fassett has joined our team as our new data science fellow.
James E Johndrow, Kristian Lum and D B Dunson (2018). Theoretical limits of microclustering for record linkage. Biometrika. 19 March 2018. © 2018 Oxford University Press. DOI 10.1093/biomet/asy003.
John E Johndrow, Kristian Lum and D B Dunson (2018). Theoretical limits of microclustering for record linkage. Biometrika. 19 March 2018. © 2018 Oxford University Press. DOI 10.1093/biomet/asy003.
Collecting and Protecting Human Rights Data in Guatemala (1991-2013)
In 1996, a peace accord brokered by the United Nations ended 36 years of internal armed conflict in Guatemala. During the hostilities, non-governmental organizations asked for technical support from the scientific community in the project to gather the experiences of witnesses and victims in databases.
From 1993 to 1999 Dr. Patrick Ball, then at the American Association for the Advancement of Science (AAAS), worked with the International Center for Human Rights Research in Guatemala (CIIDH) to collect and organize evidence of more than 43,000 human rights violations. The ...
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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. ...
ed. by Taylor B. Seybolt, Jay D. Aronson, and Baruch Fischhoff. Oxford University Press. © 2013 Oxford University Press. All rights reserved.
The following four chapters are included:
— Todd Landman and Anita Gohdes (2013). “A Matter of Convenience: Challenges of Non-Random Data in Analyzing Human Rights Violations in Peru and Sierra Leone.”
— Jeff Klingner and Romesh Silva (2013). “Combining Found Data and Surveys to Measure Conflict Mortality.”
— Daniel Manrique-Vallier, Megan E. Price, and Anita Gohdes (2013). “Multiple-Systems Estimation Techniques for Estimating Casualties in Armed Conflict.”
— Jule Krüger, Patrick Ball, Megan Price, and Amelia Hoover Green (2013). “It Doesn’t Add Up: Methodological and Policy Implications of Conflicting Casualty Data.”
Identifiers being sequential could make possible estimations of the population of detained children.
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A note for persons in search of assistance with specific human rights cases: We are very sorry for your troubles and your suffering; however, HRDAG does not take on casework. If you need help with a human rights case, you might consider requesting it from the International Committee of the Red Cross (www.icrc.org).
Photo: U.S. National Archives
The primer addresses what pretrial risk assessment is and what the research supports.
It could make sense to use Rust as a data journalist for in-browser computations, and other thoughts from RustConf.
We aim to produce code that is clear, replicatable across machines and operating systems, and that leaves an easy-to-follow audit trail.
“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.
I spent the two weeks over Easter working with Patrick and Megan in San Francisco, trying to figure out a strategy of how best to estimate the number of casualties the Syrian civil war has claimed in the past two years. In January, HRDAG published a report on the number of fully identified casualties reported in the Syrian Arab Republic between March 2011 and November 2012. The number of de-duplicated records of killings for this period was 59,648, a number that is likely to be an undercount since we know that many incidences of lethal violence in conflict go unreported, and that the unreported cases are not missing at random. (more…)
Herb led and mentored a generation of statisticians working in human rights.
The modular nature of the workflow and use of Git allowed us to work on different parts of the project from across the country.
HRDAG built a machine-learning tool to strip the raw data of any potentially identifying information such as names and court case numbers. There was no "acceptable error rate."
Algorithmic tools like PredPol were supposed to reduce bias. But HRDAG has found that racial bias is baked into the data used to train the tools.
HRDAG is currently evaluating the quality and completeness of the Kosovo Memory Book of the Humanitarian Law Center (HLC) in Belgrade, Serbia. The objective of the Kosovo Memory Book (KMB) is to commemorate every single person who fell victim to armed conflict in Kosovo from 1998 to 2000, either through death or disappearance.
While building and reviewing their database, one of the things that HLC has to do is “record linkage,” a process also known as “matching.” Matching determines whether two records are the same people (“a match”) or different people (“a non-match”). Matching helps to identify whether two existing records refer ...
