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The HRDAG Tech Corner is where we collect the deeper and geekier content that we create for the website. Click the accordion blocks below to reveal each of the Tech Corner entries.
Sifting Massive Datasets with Machine Learning
Principled Data Processing
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.
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...
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 data science field is always changing, which means that I'll always be learning.
The report tries to answer the question of whether a particular risk assessment model reinforces racial inequalities in the criminal justice system.
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...
To donate to Human Rights Data Analysis Group via a Donor Advised Fund, you should follow three steps:
In your Donor Advised Fund portal, look up our fiscal sponsor Community Partners using the Tax ID 95-4302067. It is important to use the tax ID, not the name, as there are many organizations with the same or similar names.
Once you have selected Community Partners, look for a way to customize the donation or donate to a specific project that allows you to write-in the name of a fund or project. Then please write in "Human Rights Data Analysis Group."
Please email info@hrdag.org to let us know that you have sent the donation so we can make ...
Margot is a professor in the Department of Energy Resources Engineering at Stanford University, interested in computer simulation and mathematical analysis of engineering processes.
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.”
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 justif...
The American Statistical Association’s 2018 Karl E. Peace Award for Outstanding Statistical Contributions for the Betterment of Society recently recognized the work of leading human rights mathematician Patrick Ball of the Human Rights Data Analysis Group (HRDAG). The award is presented annually to statisticians whose exemplary statistical research is matched by the impact their work has had on the lives of people.
Established by the family of Karl E. Peace in honor of his work for the good of society, the award—announced at the Joint Statistical Meetings—is bestowed upon distinguished individual(s) who have made substantial contributions to the statistical profession, contributions that have led in direct ways to improving the human condition. Recipients will have demonstrated through their accomplishments their commitment to service for the greater good.”
This year, Ball became the 10th recipient of the award. Read more …
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.
Tamy Guberek and Margaret Hedstrom (2017). On or off the record? Detecting patterns of silence about death in Guatemala’s National Police Archive. Archival Science. 9 February 2017. © Springer. DOI 10.1007/s10502-017-9274-3.
Tamy Guberek and Margaret Hedstrom (2017). On or off the record? Detecting patterns of silence about death in Guatemala’s National Police Archive. Archival Science. 9 February 2017. © Springer. DOI 10.1007/s10502-017-9274-3.
Megan Price, Anita Gohdes, and Patrick Ball. 2015. Significance 12, no. 2 (April): 14–19. doi: 10.1111/j.1740-9713.2015.00811.x. © 2015 The Royal Statistical Society. All rights reserved. [online abstract]
