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Multiple Systems Estimation: Collection, Cleaning and Canonicalization of Data
<< Previous post: MSE: The Basics
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…)
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.
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Multiple Systems Estimation: The Basics
Multiple systems estimation, or MSE, is a family of techniques for statistical inference. MSE uses the overlaps between several incomplete lists of human rights violations to determine the total number of violations. In this blogpost, and four more to follow, I’ll answer both conceptual and practical questions about this important method. (In posts to follow, questions that refer to specific statistical procedures or debates will be marked, "In depth.") (more…)
Letter from the Executive Director
Dear Friends,
This has been quite a year, and I don’t just mean the recent political events in the United States, Europe and the Middle East.
Thanks to your ongoing support, HRDAG has a number of accomplishments to be proud of this year:
Patrick’s testimony in the trial of Hissene Habré for crimes against humanity was cited by the judges three times in their determination of guilt.
We launched a book describing ten years of collaborative work with the Historic Archive of the National Police in Guatemala.
We contributed quantitative analyses to Amnesty International’s report on deaths in Syrian custody, and published an ...
Predictive Policing Reinforces Police Bias
Issues surrounding policing in the United States are at the forefront of our national attention. Among these is the use of “predictive policing,” which is the application of statistical or machine learning models to police data, with the goal of predicting where or by whom crime will be committed in the future. Today Significance magazine published an article on this topic that I co-authored with William Isaac. Significance has kindly made this article open access (free!) for all of October. In the article we demonstrate the mechanism by which the use of predictive policing software may amplify the biases that already pervade our criminal ...
The ‘Dirty War Index’ and the Real World of Armed Conflict.
Amelia Hoover, Romesh Silva, Tamy Guberek, and Daniel Guzmán. “The ‘Dirty War Index’ and the Real World of Armed Conflict.” May 23, 2009. © 2009 HRDAG. Creative Commons BY-NC-SA.
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.
Estimating Undocumented Homicides with Two Lists and List Dependence
Kristian Lum and Patrick Ball. 2015. Human Rights Data Analysis Group (April 2). © 2015 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.
First Things First: Assessing Data Quality Before Model Quality.
Anita Gohdes and Megan Price (2013). Journal of Conflict Resolution, Volume 57 Issue 6 December 2013. © 2013 Journal of Conflict Resolution. All rights reserved. Reprinted with permission of SAGE. [online abstract]DOI: 10.1177/0022002712459708.
Measuring the Mortality Consequences of Armed Conflict in Amritsar, India: A New Approach to the Indirect Sampling of Conflict-Related Mortality
Romesh Silva and Jeff Klingner. “Measuring the Mortality Consequences of Armed Conflict in Amritsar, India: A New Approach to the Indirect Sampling of Conflict-Related Mortality.” Poster presented at the Population Association of America 2011 Annual Meeting. © 2011 Benetech. Creative Commons BY-NC-SA.
The UDHR Turns 70
We're thinking about how rigorous analysis can fortify debates about components of our criminal justice system such as cash bail, pretrial risk assessment and fairness in general.
Tech Note: Chicago Missing Persons
Our team was able to identify over 50 complaints related to missing persons cases.
Learning to Learn: Reflections on My Time at HRDAG
So much of what I learned at HRDAG was intangible, and I'm grateful to have been able to go deep.
How Machine Learning Makes Visible Gender-Based Violence by Police
Sexual misconduct by police sometimes gets buried through official coding procedures. In Chicago, HRDAG processed police misconduct documents to give visibility to allegations that would otherwise be lost.
Get Involved/Donate
Donating to HRDAG
Thank you for your interest in making a donation to the Human Rights Data Analysis Group to help us use science to support our partners in the human rights world.
You can make a donation by credit card on the Community Partners® Network for Good page. HRDAG is a "project of Community Partners," and right below the section on payment information, you'll be able to select "Human Rights Data Analysis Group" from a drop-down menu. (On most browsers, if you use this link, HRDAG will be pre-selected on the drop-down menu.)
This transaction will appear on your credit card statement as "Network for Good."
If you donate by check, ...
Core Concepts
Inaccurate statistics can damage the credibility of human rights claims—and that's why we strive to ensure that statistics about human rights violations are generated with as much rigor and are as scientifically accurate as possible.
But, what are the pitfalls leading to inaccuracy—when, where, and how do data become compromised? How are patterns biased by having only partial data? And what are the best scientific methods for collecting, managing, processing and analyzing data?
Here are the data pitfalls that HRDAG has identified, as well as some of our approaches for meeting these challenges. We believe that human rights researchers must take ...
Big Data Predictive Analytics Comes to Academic and Nonprofit Institutions to Fuel Innovation
"Revolution Analytics will allow HRDAG to handle bigger data sets and leverage the power of R to accomplish this goal and uncover the truth." Director of Research Megan Price is quoted.
REVOLUTION ANALYTICS
Press release
February 4, 2014
Link to press release
Back to Press Room