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Using Math and Science to Count Killings in Syria
In this afternoon "Lightning Talk" at RightsCon 2014, Megan Price spoke about the importance of using models to adjust for variability when reporting human rights violations and mentioned innovative tools that can be used for tracking abuses.
RIGHTSCON
March 4, 2014
San Francisco, California
Link to RightsCon program
Back to Talks
Data and Social Good: Using Data Science to Improve Lives, Fight Injustice, and Support Democracy
In this free, downloadable report, Mike Barlow of O’Reilly Media cites several examples of how data and the work of data scientists have made a measurable impact on organizations such as DataKind, a group that connects socially minded data scientists with organizations working to address critical humanitarian issues. HRDAG—and executive director Megan Price—is one of the first organizations whose work is mentioned.
Using Data and Statistics to Bring Down Dictators
In this story, Guerrini discusses the impact of HRDAG's work in Guatemala, especially the trials of General José Efraín Ríos Montt and Colonel Héctor Bol de la Cruz, as well as work in El Salvador, Syria, Kosovo, and Timor-Leste. Multiple systems estimation and the perils of using raw data to draw conclusions are also addressed.
Megan Price and Patrick Ball are quoted, especially in regard to how to use raw data.
“From our perspective,” Price says, “the solution to that is both to stay very close to the data, to be very conservative in your interpretation of it and to be very clear about where the data came from, how it was collected, what ...
Guatemala 1993-1999 – Using MSE to Estimate the Number of Deaths
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 ...
Talks & Discussions
2021 Rafto Prize Videos
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Lessons at HRDAG: Making More Syrian Records Usable
If we could glean key missing information from those fields, we would be able to use more records.
Analyzing patterns of violence in Colombia using more than 100 databases
The institution’s objectives were to learn the truth about what happened during the armed conflict.
In Colombia: HRDAG and Dejusticia on the Importance of Missing Data
It’s inevitable that databases will have information gaps, and special care must be taken to account for these gaps.
Talks
Upcoming Talks
TBA
Past Talks
2015
Presentation on the research behind the Evaluation of the Kosovo Memory Book Database. National Archive, Pristina, Kosovo. Patrick Ball. February 4, 2015.
How do we know what we know? Patrick Ball. Arizona State University. January, 2015.
AAAS Science & Human Rights Coalition Meeting: Big Data & Human Rights. Megan Price, panelist. Washington, D.C. January 15-16, 2015.
Examining the Crisis in Syria: Conference Hosted by New America and Arizona State University’s Center on the Future of War and the Walter Cronkite School of Journalism and Mass Communication. Megan Price, panelist. Washingt...
New results for the identification of municipalities with clandestine graves in Mexico
The goal of this project is identify Mexican municipalities with a high probability of having clandestine graves. Knowing where to search will help to create better public programs regarding missing persons in Mexico.
Data on Kosovo – Other
The other data is in three files. All of the files are comma-delimited UTF-8 (like ASCII but including the characters to render Serbian names). The fields in each file are described below.
If you use these data, please cite them with the following citation, as well as this note:
“These are convenience sample data, and as such they are not a statistically representative sample of events in this conflict. These data do not support conclusions about patterns, trends, or other substantive comparisons (such as over time, space, ethnicity, age, etc.).”
Human Rights Data Analysis Group. (2002). Database of NATO airstrikes, geographic coding, and KLA ...
Using Machine Learning to Help Human Rights Investigators Sift Massive Datasets
How we built a model to search hundreds of thousands of text messages from the perpetrators of a human rights crime.
Using MSE to Estimate Unobserved Events
At HRDAG, we worry about what we don't know. Specifically, we worry about how we can use statistical techniques to estimate homicides that are not observed by human rights groups. Based on what we've seen studying many conflicts over the last 25 years, what we don't know is often quite different from what we do know.
The technique we use most often to estimate what we don't know is called "multiple systems estimation." In this medium-technical post, I explain how to organize data and use three R packages to estimate unobserved events.
Click here for Computing Multiple Systems Estimation in R.
The task is a quantum of workflow
This post describes how we organize our work over ten years, twenty analysts, dozens of countries, and hundreds of projects: we start with a task. A task is a single chunk of work, a quantum of workflow. Each task is self-contained and self-documenting; I'll talk about these ideas at length below. We try to keep each task as small as possible, which makes it easy to understand what the task is doing, and how to test whether the results are correct.
In the example I'll describe here, I'm going to describe work from our Syria database matching project, which includes about 100 tasks. I'll start with the first thing we do with files we receive ...
A Model to Estimate SARS-CoV-2-Positive Americans
We’ve built a model for estimating the true number of positives, using what we have determined to be the most reliable datasets—deaths.
Kosovo 1999 – Using MSE to Examine Political Claims
Patrick Ball expanded his use of multiple systems estimation (MSE) to clarify the history of a deadly conflict in Kosovo. The violence began in 1989 when Serbian President Slobodan Milošević revoked Kosovo's autonomous status within the Republic of Serbia triggering fighting between Kosovar Albanians and the Yugoslav government. Allegations of widespread and systematic human rights violations were made against Serbian forces and NATO intervened to repel Serb forces from Kosovo. Ball and Scheuren gathered data from Albanian border crossings and other sources in the region. They used this information to examine the claim by the Yugoslav government ...
12 Questions about Using Data Analysis to Bring Guatemalan War Criminals to Justice
When people talk about war criminals in Guatemala, which war are they talking about?
They’re talking about the Guatemalan civil war, which began in 1960 and ended in 1996. That’s thirty-six years of civil war. Even though it ended almost two decades ago, Guatemala is still recovering from it. At its simplest, this civil war story was right-wing government forces fighting leftist rebels. But it went deeper than that, of course. The majority of the rebel forces was composed of indigenous peoples, primarily the Maya, (more…)
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 ...
Tech Corner
The HRDAG Tech Corner is where we collect the deeper and geekier content that we create for the website. Click the accordion blocks below to reveal each of the Tech Corner entries.
Sifting Massive Datasets with Machine Learning
Principled Data Processing