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Release of Yellow Book Calls on Salvadoran Military to Open Archives
Applications of Multiple Systems Estimation in Human Rights Research
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].
The World According to Artificial Intelligence (Part 1)
The World According to Artificial Intelligence: Targeted by Algorithm (Part 1)
The Big Picture: The World According to AI explores how artificial intelligence is being used today, and what it means to those on its receiving end.
Patrick Ball is interviewed: “Machine learning is pretty good at finding elements out of a huge pool of non-elements… But we’ll get a lot of false positives along the way.”
The UDHR Turns 70
Welcome!
Transitional Justice in Syria: Accountability and Reconciliation Conference
Perú
The Great Lessons in Research at the Archive
Why It Took So Long To Update the U.N.-Sponsored Syria Death Count
In this story, Carl Bialik of FiveThirtyEight interviews HRDAG executive director Patrick Ball about the process of de-duplication, integration of databases, and machine-learning in the recent enumeration of reported casualties in Syria.
New reports of old deaths come in all the time, Ball said, making it tough to maintain a database. The duplicate-removal process means “it’s a lot like redoing the whole project each time,” he said.
Download: Megan Price
Executive director Megan Price is interviewed in The New York Times’ Sunday Review, as part of a series known as “Download,” which features a biosketch of “Influencers and their interests.”
How Structuring Data Unburies Critical Louisiana Police Misconduct Data
Unbiased algorithms can still be problematic
“Usually, the thing you’re trying to predict in a lot of these cases is something like rearrest,” Lum said. “So even if we are perfectly able to predict that, we’re still left with the problem that the human or systemic or institutional biases are generating biased arrests. And so, you still have to contextualize even your 100 percent accuracy with is the data really measuring what you think it’s measuring? Is the data itself generated by a fair process?”
HRDAG Director of Research Patrick Ball, in agreement with Lum, argued that it’s perhaps more practical to move it away from bias at the individual level and instead call it bias at the institutional or structural level. If a police department, for example, is convinced it needs to police one neighborhood more than another, it’s not as relevant if that officer is a racist individual, he said.