275 results for search: Что такое меркантильные интересы больше в insta---batmanapollo/feed/inter-rater-reliability
“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.
The primer addresses what pretrial risk assessment is and what the research supports.
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.
The modular nature of the workflow and use of Git allowed us to work on different parts of the project from across the country.
HRDAG is currently evaluating the quality and completeness of the Kosovo Memory Book of the Humanitarian Law Center (HLC) in Belgrade, Serbia. The objective of the Kosovo Memory Book (KMB) is to commemorate every single person who fell victim to armed conflict in Kosovo from 1998 to 2000, either through death or disappearance.
While building and reviewing their database, one of the things that HLC has to do is “record linkage,” a process also known as “matching.” Matching determines whether two records are the same people (“a match”) or different people (“a non-match”). Matching helps to identify whether two existing records refer ...
In 2020, HRDAG provided clarity on issues related to the pandemic, police misconduct, and more.
Text in English
[popup citation="Tamy Guberek, Daniel Guzmán, Megan Price, Kristian Lum and Patrick Ball. (2010). Benetech/Human Rights Data Analysis Group database of lethal violence in Casanare."]
Estimaciones de Homicidios y Desapariciones en Casanare
Casanare es un departamento extenso y rural de Colombia con 19 municipios y una población de casi 300.000 habitantes. Ubicado entre las faldas de los Andes y las planicies orientales, Casanare tiene una larga historia de violencia. Diversos grupos armados han hecho presencia en Casanare, entre ellos paramilitares, guerrillas y el ejército colombiano. Muchos habitantes del Casanare han sido vícti...
Dr. Patrick Ball recently visited the Plutopia News Network podcast for a wide-ranging, inspiring conversation about his work for the Human Rights Data Analysis Group.
Patrick spoke about how he first discovered human rights work during his time in El Salvador with the Peace Brigades International. That led to his ongoing work as a statistician and computer programmer working to assess and analyze human rights violations. He also unpacked some common statistical techniques used by researchers at Human Rights Data Analysis Group, such as multiple systems estimation, which uses multiple different datasets to gain insights into the data we don't ...
I had the pleasure of working with Patrick Ball at the HRDAG office in San Francisco for a week during summer 2016. I knew Patrick from two workshops he previously hosted at the University of Washington’s Centre for Human Rights (UWCHR). The workshops were indispensable to us at UWCHR as we worked to publish a number of datasets on human rights violations during the El Salvador Civil War. The training was all the more helpful because the HRDAG team was so familiar with the data. As part of an impressive career which took him from Ethiopia and Kosovo to Haiti and El Salvador among others, Patrick himself had worked on gathering and analysing ...
Sloppy recordkeeping by Chicago police has compromised missing persons cases. HRDAG is working with Pulitzer Prize-winning Invisible Institute to shed light on these stories.
HRDAG is pleased to accept donations to our nonprofit via Donor Advised Funds. We’ve published step-by-step directions to make donating through a DAF simple and fast. Check out the directions now or contact us at info@hrdag.org if you have any questions.
October 9 is Donor Advised Fund Day—a recently established awareness day that encourages DAF holders to activate their funds by recommending charities to receive support. You can participate by donating to HRDAG via a Donor Advised Fund or read on to learn more.
What is a Donor Advised Fund?
A Donor-Advised Fund (DAF) is an easy, flexible way to support charities while taking advantage of ...
Structural Zero Issue 04
September 30, 2025
Artificial intelligence is transforming how we work with information. At HRDAG, that changes how I do my job every day. My most recent project was using LLMs to explore and parse vast quantities of data about police abuses in California.
In this newsletter, I’ll pull back the curtain on that work. I’ll describe how a diverse coalition gathered more than a million pages of documents about police misconduct in California and how LLMs helped us make sense of them in ways that wouldn’t have been possible before the advent of this technology.
In addition to understanding my work, I hope that this ...
Structural Zero Issue 03
August 24, 2025
Part Three of Our Three-Part “Gathering the Data” Series. Read part one and part two.
In computer security, “security” is always relative to something. What are we actually defending against, and how are we doing it? This is our “threat model.”
My colleagues and I have been using scientific tools to analyze evidence of human rights abuses, including using statistics to uncover mass graves in Mexico and analyzing under-reported police homicides in the United States.
Our work isn’t always popular. It can infuriate those in power who want to cover up incriminating truths about the ...
Some police officers avoid accountability by “wandering” to another agency. HRDAG and partners created a data tool that tracks officers’ employment history.
New paper in Biometrika, co-authored by HRDAG's Kristian Lum and James Johndrow: Theoretical limits of microclustering in record linkage.
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.
Illuminating Data's Dark Side: Big data create conveniences, but we must consider who designs these tools, who benefits from them, and who is left out of the equation.
An excerpt: The “It breaks the human” report released by the human rights group Amnesty International highlights new statistics from the Human Rights Data Analysis Group, or HRDAG, an organization that uses scientific approaches to analyze human rights violations.
But while HRDAG’s estimate relied on the painstaking efforts of human workers to carefully weed out potential duplicate records, hashing with statistical estimation proved to be faster, easier and less expensive. The researchers said hashing also had the important advantage of a sharp confidence interval: The range of error is plus or minus 1,772, or less than 1 percent of the total number of victims.
“The big win from this method is that we can quickly calculate the probable number of unique elements in a dataset with many duplicates,” said Patrick Ball, HRDAG’s director of research. “We can do a lot with this estimate.”
