If we could glean key missing information from those fields, we would be able to use more records.
Today Guatemala’s former national police chief Colonel Héctor Rafael Bol de la Cruz was convicted and sentenced to 40 years in prison for his role in the 1984 kidnapping and disappearance of 27-year-old student union leader Fernando Garcia, who was last seen when officers detained him outside his home. Along with Bol de la Cruz, former senior police officer Jorge Gomez was also tried; he received a sentence of 40 years in prison. That verdict comes in part because of testimony this month by HRDAG’s Patrick Ball, who served as an expert witness and presented data analysis done with colleague Daniel Guzmán to assess the flow of thousands of ...
On November 4, 2015, the BMJ published our "Rapid Response" to Civilian deaths from weapons used in the Syrian conflict (BMJ 2015;351:h4736). The response was co-authored by Megan Price, Anita Gohdes, Jay Aronson (Carnegie Mellon University, Center for Human Rights Science), and Christopher McNaboe (Carter Center, Syria Conflict Mapping Project).
We have three concerns about this article. First, the article apportions responsibility for casualties to particular perpetrator organizations based on a single snapshot of territorial control that ignores the numerous (and well-documented) changes in this phenomenon over time. Second, combining Syrian ...
New paper in Biometrika, co-authored by HRDAG's Kristian Lum and James Johndrow: Theoretical limits of microclustering in record linkage.
Last month Significance magazine published an article on the topic of predictive policing and police bias, which I co-authored with William Isaac. Since then, we've published a blogpost about it and fielded a few recurring questions. Here they are, along with our responses.
Do your findings still apply given that PredPol uses crime reports rather than arrests as training data?
Because this article was meant for an audience that is not necessarily well-versed in criminal justice data and we were under a strict word limit, we simplified language in describing the data. The data we used is a version of the Oakland Police Department’s crime report...
How might we learn what we don’t know? HRDAG associate Christine Grillo hits the wayback machine and recalls her first exposure to People Against Bad Things, ideas about bias and correlation versus causation, and truth.
The report tries to answer the question of whether a particular risk assessment model reinforces racial inequalities in the criminal justice system.
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...
Margot is a professor in the Department of Energy Resources Engineering at Stanford University, interested in computer simulation and mathematical analysis of engineering processes.
The data science field is always changing, which means that I'll always be learning.
This morning I got a query from a journalist asking for our data from the report we published yesterday. The journalist was hoping to create an interactive infographic to track the number of deaths in the Syrian conflict over time. Our data would not support an analysis like the one proposed, so I wrote this reply.
We can't send you these data because they would be misleading—seriously misleading—for the purpose you describe. Here's why:
What we have is a list of documented deaths, in essence, a highly non-random sample, though a very big one. We like bigger samples because we think that they must be closer to true. The mathematical justificat...
Kristian Lum: “The historical over-policing of minority communities has led to a disproportionate number of crimes being recorded by the police in those locations. Historical over-policing is then passed through the algorithm to justify the over-policing of those communities.”
Megan Price and Patrick Ball (2014). SAIS Review of International Affairs © 2014 The Johns Hopkins University Press. This article first appeared in SAIS Review, Volume 34, Issue 1, Winter-Spring 2014, pages 9-20. All rights reserved.
Anita Gohdes and Megan Price (2013). Journal of Conflict Resolution, Volume 57 Issue 6 December 2013. © 2013 Journal of Conflict Resolution. All rights reserved. Reprinted with permission of SAGE. [online abstract]DOI: 10.1177/0022002712459708.
This is the fourth ALGO story.
HRDAG and our partners Data Cívica and the Iberoamericana University created a machine-learning model to predict which counties (municipios) in Mexico have the highest probability of unreported hidden graves. The predictions help advocates to bring public attention and government resources to search for the disappeared in the places where they are most likely to be found.
Context
For more than ten years, Mexican authorities have been discovering hidden graves (fosas clandestinas). The casualties are attributed broadly—and sometimes inaccurately—to the country’s “drug war,” but the motivations and perpetrators behind the mass murders ...
Lum, Kristian, Megan Emily Price, and David Banks. 2013. The American Statistician 67, no. 4: 191-200. doi: 10.1080/00031305.2013.821093. © 2013 The American Statistician. All rights reserved. [free eprint may be available].