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So much of what I learned at HRDAG was intangible, and I'm grateful to have been able to go deep.
“The model uses obvious predictor variables, Ball says, such as whether or not a drug lab has been busted in that county, or if the county borders the United States, or the ocean, but also includes less-obvious predictor variables such as the percentage of the county that is mountainous, the presence of highways, and the academic results of primary and secondary school students in the county.”
On March 16, Kristen Yawitz joined the HRDAG team in the role of Foundation Relations and Strategy Lead.
Larry Barrett has joined HRDAG as a Human Rights and Data Science Intern until February, 2022.
Alanna Flores joins HRDAG for the summer as a Data Science Fellow.
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 ...
In our database deduplication work, we’re trying to figure out which records refer to the same person, and which other records refer to different people.
We write software that looks at tens of millions of pairs of records. We calculate a model that assigns each pair of records a probability that the pair of records refers to the same person. This step is called pairwise classification.
However, there may be more than just one pair of records that refer to the same person. Sometimes three, four, or more reports of the same death are recorded.
So once we have all the pairs classified, we need to decide which groups of records refer to the ...
HRDAG's advisory board has added three new members.
Three months after the announcement of the momentous verdict finding former Chadian president Hissène Habré guilty of crimes against humanity, the presiding judges have released the full, written 681-page judgment of the court. Testimony given by HRDAG’s director of research, Patrick Ball, is mentioned at three points in the verdict.
The judges included in their written judgment the HRDAG analysis that the mortality rate in Habré prisons was staggeringly high—much higher than the mortality rate among the population as a whole. Here’s an excerpt from the judgment, page 358 (translated by Google):
The statistical expert, Patrick Ball, ...
Last week Forensis, the Colombian National Institute of Forensic Medicine’s flagship publication, published the first of our analyses of homicide patterns in Colombia. Authored by HRDAG executive director Patrick Ball and UN colleague Michael Reed Hurtado, “Cuentas y mediciones de la criminalidad y de la violencia” (pages 529-545) explores, as the title suggests, the quality of “truth” contained within crime registries. Citing the problem of partial data, missing data, and inherent design bias, Patrick and Michael write that no register, official or unofficial, can present a true reflection of what has really happened.
This publication...
Elizabeth Eagen of the Citizens and Technology Lab at Cornell University will expand the HRDAG advisory board.
Paula Amado has joined as a Research Scholar, and María Juliana Durán Fedullo has joined as a Visiting Scholar.
The institution’s objectives were to learn the truth about what happened during the armed conflict.
A week in the California redwoods amongst a hodgepodge of people united by their passion for using quantitative analysis to combat injustice.
Our partners were eager to learn and talk about emerging decentralized technology.
Principled Data Processing is a way to prove to someone, usually yourself, that what you did was right.
Bailey joined HRDAG as a data scientist in 2022.
Help HRDAG use data science to work for justice, accountability, and human rights. We are nonpartisan and nonprofit, but we are not neutral; we are always on the side of human rights.
Cryptocurrency donations to 501(c)3 charities receive the same tax treatment as stocks. Your donation is a non-taxable event, meaning you do not owe capital gains tax on the appreciated amount and can deduct it on your taxes. This makes Bitcoin and other cryptocurrency donations one of the most tax efficient ways to support us.
We are a team of experts in machine learning, applied and mathematical statistics, computer science, demography, and social science, and ...
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.
From the article: “As we seek to advance the responsible use of data for racial injustice, we encourage individuals and organizations to support and build upon efforts already underway.” HRDAG is listed in the Data Driven Activism and Advocacy category.
