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HRDAG To Join the Partnership on AI
HRDAG is joining Partnership on AI to Benefit People and Society (PAI).
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.
Herb Spirer, 1925 – 2018
Herb led and mentored a generation of statisticians working in human rights.
Announcing New HRDAG Advisory Board Member
Elizabeth Eagen of the Citizens and Technology Lab at Cornell University will expand the HRDAG advisory board.
Happy Hacking
From my first introduction to the HRDAG community at the annual retreat it was clear to me that mentorship is an organizational priority and that the contributions of interns are valued. Much of my first couple weeks as a summer intern at HRDAG were spent familiarizing myself with Patrick’s paradigm for principled data processing. At the same time, I was learning the skills and tricks (bash, make, vim, git) that promote an effortless programming workflow, a pursuit that Patrick calls “sharpening the saw” (just like in programming, you can cut down a tree with a dull blade, but your life will be much easier if you take the time to sharpen ...
Kosovo Data – Killings, Migrations and More
Much of the debate about the March–June 1999 war between NATO and Yugoslavia turned on how many people left their homes in particular places and at certain times. Solid information about the flow of refugees out of Kosovo has helped investigators to link patterns in the flow to patterns of NATO bombing, Yugoslav strategic plans for "cleansing" Kosovo, and Yugoslav and irregular troop deployments. At its heart, the debate was about whether refugees left their homes fleeing NATO attacks and fighting between the KLA and Yugoslav forces, or whether they left their homes after being threatened, assaulted, and robbed by Yugoslav police, army, and irregu...
Megan Price: Life-Long ‘Math Nerd’ Finds Career in Social Justice
“I was always a math nerd. My mother has a polaroid of me in the fourth grade with my science fair project … . It was the history of mathematics. In college, I was a math major for a year and then switched to statistics.
I always wanted to work in social justice. I was raised by hippies, went to protests when I was young. I always felt I had an obligation to make the world a little bit better.”
Data on Kosovo Migration
[popup citation="For migrations: Ball, Patrick. (2000). AAAS/ Human Rights Data Analysis Group database of migrations in Albania and Kosovo. For killings: Patrick Ball, Wendy Betts, Fritz Scheuren, Jana Dudukovich, and Jana Asher. (2002). AAAS/ABA-CEELI/Human Rights Data Analysis Group database of killings in Kosovo. For other data: Human Rights Data Analysis Group. (2002). Database of NATO airstrikes, geographic coding, and KLA activity in Kosovo."]
The data on migration from Kosovo are in seven files. All of the files are comma-delimited ASCII. The fields in each file are described below. For more information, see Policy or Panic, section A1, pp. ...
HRDAG Retreat 2014
Ten data nerds gathered in a large hilltop beach house to analyze counts of killings from several war-torn countries. The time was January 16-20, 2014, the place was near San Francisco, the agenda was packed, and I was excited to be there.
Having defended my dissertation at Carnegie Mellon University just days before, I had often supposed that my thesis on a generalization of
log-linear models for capture-recapture might serve little other purpose than to fill a line on my curriculum vitae. This perception faded after a mid-2013 discussion with Patrick convinced me that HRDAG's data challenges could easily be the best match to my research ...
Emeritus Advisers
We are grateful to our past advisers for their contributions to HRDAG.
Advisory Board
Audrey Chapman, Healey Chair in Medical Humanities and Bioethics, University of Connecticut
2013-2014 (one-year term)
CIIDH Data – Dictionary
Version date: 2000.01.29
Current version: ATV20.1
Patrick Ball & Herbert F. Spirer
The unit of analysis for each record in this structure is VIOLATION.
Each violation was of a particular type, happened at a particular time and place, and was committed by zero, one, or several organizational perpetrators. The violation was committed against zero or one named (individually identified) victim, and zero or more anonymous (unidentified) additional victims. The violation was reported one or more times in one, two, or three source types.
Note that to count the number of times individuals suffered particular violations, users should sum either the ...
Welcoming our new Technical Lead
After almost two months of searching for the perfect fit, we’re very pleased to announce that Josh Shadlen has joined HRDAG as our new technical lead. Finding Josh was no easy feat. We were looking for what many people would call a “data scientist,” that is, someone with expertise in both computer science and statistics. These days, “data science” is one of the hottest fields out there.
Bringing the perfect mix of academic depth and thoughtful reflection, Josh stood out for us. With prior jobs including gigs at Silicon Valley startups and Twitter, he’s got high-level (more…)
The case of Ana Lucrecia Orellana Stormont
When working with documents in an archive, every document offers the opportunity for statistical study and quantitative research. But a document can also offer the discovery of a story. That is the case with the disappearance of Ana Lucrecia Orellana Stormont, who was reported missing on June 6, 1983, at the age of 35.
Ana Lucrecia, a professor of psychology at the University of San Carlos, was scheduled to attend a meeting with Edgar Raúl Rivas Rodríguez at the Plaza Hotel in Guatemala’s capital city. Edgar, who also went missing, was a teacher at the School of Political Science at the University. (Ana Lucrecia’s case is explained more fully ...
FAT* Conference 2018
Kristian Lum spoke about "Understanding the Context and Consequences of Pre-Trial Detention" at the Conference on Fairness, Accountability, and Transparency (FAT*).
HRDAG Retreat 2018
What follows is an elaborate criss-crossing of collaborations—retreat is a time to embrace the productivity that comes with being in the same room.
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 ...
Convenience Samples: What they are, and what they should (and should not) be used for
As noted on our Core Concepts page, we spend a lot of time worrying about the ways data are used to make claims about human rights violations. This is because inaccurate statistics can damage the credibility of human rights claims. Analyses of records of human rights violations are used to guide policy decisions, determine resource allocation for interventions, and inform transitional justice mechanisms. It is vital that such analyses are accurate.
Unfortunately, all too often these decisions are based, inappropriately, on analyses of a single convenience sample. (more…)
Reflections: A Love Letter to HRDAG
On the anniversary of the Universal Declaration of Human Rights, HRDAG executive director Megan Price tells us why she loves her work, and why she feels hopeful about the future.
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.
Reflections: Pivotal Moments in Freetown
The summer of 2002 in Washington, DC, was steamy and hot, which is how I remember my introduction to HRDAG. I had begun working with them, while they were still at AAAS, in the late spring, learning all about their core concepts: duplicate reporting and MSE, controlled vocabularies, inter-rater reliability, data models and more. The days were long, with a second shift more often than not running late into the evening. In addition to all the learning, I also helped with matching for the Chad project – that is, identifying multiple records of the same violation – back when matching was done by hand. But it was not long after I arrived in Washington ...