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Core Concepts
Inaccurate statistics can damage the credibility of human rights claims—and that's why we strive to ensure that statistics about human rights violations are generated with as much rigor and are as scientifically accurate as possible.
But, what are the pitfalls leading to inaccuracy—when, where, and how do data become compromised? How are patterns biased by having only partial data? And what are the best scientific methods for collecting, managing, processing and analyzing data?
Here are the data pitfalls that HRDAG has identified, as well as some of our approaches for meeting these challenges. We believe that human rights researchers must take ...
Partners
How we work with partners is how we relate to the whole human rights community. We work with human rights advocates and defenders to support their goals by complementing their substantive expertise with our technical expertise. To date, partners have included truth commissions, international criminal tribunals, United Nations missions, and non-governmental human rights organizations on five continents.
Here are a few stories that illustrate how we work with our partners:
HRDAG partner stories:
Quantifying Police Misconduct in Louisiana (2023)
Scraping for Pattern: Protecting Immigrant Rights in Washington State (2022)
Police Violence ...
Policing
If you'd like to support HRDAG in this project, please consider making a donation via Our Donate page.
Over the last year, HRDAG has deepened the national conversation about homicides by police, predictive policing software, and the role that bail plays in the criminal justice system. Our studies describe how the racial bias inherent in police practice becomes data input to predictive policing tools. In another project, we are shining light on the iniquities of bail decisions.
TEAM
Click each team member's photo for full bio. Here's the team on Twitter.
Examining the Impact of Bail
When a defendant is detained before trial, she will face ...
Coming soon: HRDAG 2019 Year-End Review
The online version of the 2019 Year-End Review will appear in January 2020.
The Statistics of Mortality Due to Conflict in Peru
A key point is that human rights data collection prior to the TRC largely ignored violence by the Shining Path.
How Machine Learning Makes Visible Gender-Based Violence by Police
Sexual misconduct by police sometimes gets buried through official coding procedures. In Chicago, HRDAG processed police misconduct documents to give visibility to allegations that would otherwise be lost.
Las cifras de la CVR en el 2019
Las estimaciones se estratificaron por ubicación y perpetrador.
Celebrating Ten Years of Data from the AHPN
Ten years ago, in July 2005, human rights officers stumbled upon a nondescript warehouse in a commercial zone of Guatemala City and changed history. They had discovered an archive–its existence kept secret–belonging to the Guatemalan National Police, whose officers committed human rights atrocities on behalf of the government during the civil war.
Inside the building was the bureaucratic detritus typical of a large government agency: 80 million pages detailing shifts worked, tasks assigned, assignments fulfilled, workers’ whereabouts, and who was supervising whom. The documents, which were found stacked on dirty floors, shoved into bags, ...
Making Missing Data Visible in Colombia
Valentina Rozo Ángel has worked with HRDAG and the Colombian Truth Commission to acknowledge victims of the 50-year conflict who are not visible or easily counted.
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 ...
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.
Multiple Systems Estimation: Collection, Cleaning and Canonicalization of Data
<< Previous post: MSE: The Basics
Q3. What are the steps in an MSE analysis?
Q4. What does data collection look like in the human rights context? What kind of data do you collect?
Q5. [In depth] Do you include unnamed or anonymous victims in the matching process?
Q6. What do you mean by "cleaning" and "canonicalization?"
Q7. [In depth] What are some of the challenges of canonicalization? (more…)
Learning to Learn: Reflections on My Time at HRDAG
So much of what I learned at HRDAG was intangible, and I'm grateful to have been able to go deep.
Reflections: Challenging Tasks and Meticulous Defenders
I have made it my personal objective to amplify HRDAG's message of being extra careful and scientifically rigorous with human rights data.
CIIDH Data – Value Labels
Version date: 2000.01.29
Current version: ATV20.1
Patrick Ball & Herbert F. Spirer
v_ind
-------------+-----------
Victim |
Ethnic |
category |
| Freq.
-------------+-----------
1 Indigenous | 2,722
2 Ladino | 1,014
3 Unknown | 13,687
|
Total | 17,423
-------------+-----------
v_sex
----------+-----------
Victim |
Sex | Freq.
----------+-----------
4 F | 2,001
5 M | 11,445
6 d | 3,977
|
Total | 17,423
----------+-----------
v_eth
-------------+-----------
Victim |
Maternal |
language ...
Multiple Systems Estimation: Stratification and Estimation
<< Previous post, MSE: The Matching Process
Q10. What is stratification?
Q11. [In depth] How do HRDAG analysts approach stratification, and why is it important?
Q12. How does MSE find the total number of violations?
Q13. [In depth] What are the assumptions of two-system MSE (capture-recapture)? Why are they not necessary with three or more systems?
Q14. What statistical model(s) does HRDAG typically use to calculate MSE estimates? (more…)
Uncovering Police Violence in Chicago: A collaboration between HRDAG and Invisible Institute
In 2014 and again in 2020, the Invisible Institute, a Chicago grassroots organization, won lawsuits that granted them access to decades of complaints of misconduct by Chicago police officers. The collection contains hundreds of thousands of pages of allegation forms, memos, various police administrative forms, interviews and testimonies, pictures, and even embedded audio files. The Institute published scanned images on the Citizens Police Data Project, and is using them for a project with HRDAG known as Beneath the Surface, which is a detailed investigation into gender-based violence by Chicago Police.
Image: David Peters
Often, gender-b...
Always Learning
The data science field is always changing, which means that I'll always be learning.
Tech Note: Chicago Missing Persons
Our team was able to identify over 50 complaints related to missing persons cases.