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Funding
HRDAG’s funding comes from private, international donors: the John D. and Catherine T. MacArthur Foundation, Open Society Foundations, an anonymous U.S.-based private foundation, Ford Foundation, The National Endowment for Democracy and individual donors. This funding supports both specific projects, as well as our scientific work generally in human rights data analysis.
For the entirety of its existence, HRDAG has been a project of non-profit organizations, first at the American Association for the Advancement of Science (AAAS), and then at Benetech, a non-profit Silicon Valley technology company. In February 2013, HRDAG became ...
Remembering Scott Weikart
HRDAG’s core values all have a connection to Scott Weikart, 1951–2023.
Newsletters
2025
21 October, 2025 - HRDAG takes a stand against tyranny
9 October, 2025 - Unlocking unstructured data and getting it right
30 September, 2025 - Structural Zero 04: Pulling Back the Curtain on LLMs and Policing Data
23 September, 2025 - California’s Racial Justice Act and Police Records Access Project
24 August, 2025 - Structural Zero 03: Without Encryption, My Work Wouldn’t Be Possible
17 July, 2025 - Structural Zero 02: Scatter and keep working
3 June, 2025 - Structural Zero 01: Dictatorships create a lot of data
24 June, 2025 - Breaking through the noise with evidence
19 May, 2025 - Structural Zero 00: Introd...
Frequently Asked Questions
Multiple Systems Estimation
What is MSE?
What do you mean by statistical inference?
What is an overlap, and how do we know when lists overlap?
How does MSE find the total number of violations?
How was MSE originally developed?
How does the Benetech Human Rights Program use MSE?
1. What is MSE?
A: 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.
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2. What do you mean by statistical inference?
A: ...
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…)
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…)
Our Thoughts on the Violence in Charlottesville
This week, we join our friends and colleagues in feeling horrified by the violence in Charlottesville, Virginia. As we have for the past 26 years, we stand with the victims of violence and support human rights and dignity for all. We spend our careers observing and documenting mass political violence across the world. The demands by the so-called “alt-right” to normalize racism and social exclusion are all too familiar to us.
At HRDAG, our work is always guided by the Universal Declaration of Human Rights (UDHR). We reaffirm our commitment to these principles, in particular that the “recognition of the inherent dignity and of the equal and ...
RustConf 2019, and systems programming as a data scientist
It could make sense to use Rust as a data journalist for in-browser computations, and other thoughts from RustConf.
Learning a Modular, Auditable and Reproducible Workflow
The modular nature of the workflow and use of Git allowed us to work on different parts of the project from across the country.
.Rproj Considered Harmful
We aim to produce code that is clear, replicatable across machines and operating systems, and that leaves an easy-to-follow audit trail.
Herb Spirer, 1925 – 2018
Herb led and mentored a generation of statisticians working in human rights.
Identifiers of Detained Children Have Implications for Data Security and Estimation
Identifiers being sequential could make possible estimations of the population of detained children.
Primer to Inform Discussions about Bail Reform
The primer addresses what pretrial risk assessment is and what the research supports.
Connect with HRDAG
If you’d like to stay informed about HRDAG events, blogposts, and news, connect with us on Twitter, Facebook or through our RSS feed. We also have a LinkedIn page.
You may contact us directly via email at info @ hrdag.org.
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
Rise of the racist robots – how AI is learning all our worst impulses
“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.
BJS Report on Arrest-Related Deaths: True Number Likely Much Greater
(This post is co-authored by Patrick Ball and Kristian Lum.)
Today the Bureau of Justice Statistics (BJS) released a report on their effort to document “all deaths that occur during the process of arrest in the United States.” The analysis estimates that the Arrest-Related Deaths (ARD) program covers only 34-49% of these deaths. A parallel program by the FBI (the Supplementary Homicide Reports, SHR) is estimated to cover approximately the same proportion of deaths. Even taking into consideration both programs, 28% of all police homicides remain unreported.
In order to estimate the total number of homicides that appear on neither the ARD or ...
How we go about estimating casualties in Syria—Part 1
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…)
How Pretrial Risk Assessment Tools Perpetuate Unfairness
Tools like Compas allegedly help judges predict future criminal activities and eliminate bias. HRDAG and partners showed how the tools recycle bias.
How Predictive Policing Reinforces Bias
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.