683 results for search: %E3%80%8E%EB%85%BC%EC%82%B0%EB%8C%80%ED%99%94%E3%80%8F%20%D5%956%D5%95%E3%85%A19%D5%952%E3%85%A18998%20%EB%8F%BC%EC%A7%80%EB%9D%A0%EB%8F%8C%EC%8B%B1%EB%AA%A8%EC%9E%84%20%EC%84%B1%EC%9D%B8%EB%8D%B0%EC%9D%B4%ED%8C%85%E2%9C%BA%EB%AA%B8%EB%A7%A4%EB%85%80%EB%A7%8C%EB%82%A8%E3%8B%97%EB%B9%84%EA%B3%B5%EA%B0%9C%EB%8C%80%ED%99%94%20%E3%83%90%E5%84%AD%20herbarium/feed/content/colombia/SV-report_2011-04-26.pdf
How many people are going to die from COVID-19?
Patrick Ball, Kristian Lum, Tarak Shah and Megan Price (2020). How many people are going to die from COVID-19? Granta. 14 March 2020. © Granta Publications 2020.
Reality and risk: A refutation of S. Rendón’s analysis of the Peruvian Truth and Reconciliation Commission’s conflict mortality study
Daniel Manrique-Vallier and Patrick Ball (2019). Reality and risk: A refutation of S. Rendón’s analysis of the Peruvian Truth and Reconciliation Commission’s conflict mortality study. Research & Politics, 22 March 2019. © Sage Journals. https://doi.org/10.1177/2053168019835628
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.”
Nonprofits Are Taking a Wide-Eyed Look at What Data Could Do
In this story about how data are transforming the nonprofit world, Patrick Ball is quoted. Here’s an excerpt: “Data can have a profound impact on certain problems, but nonprofits are kidding themselves if they think the data techniques used by corporations can be applied wholesale to social problems,” says Patrick Ball, head of the nonprofit Human Rights Data Analysis Group.
Companies, he says, maintain complete data sets. A business knows every product it made last year, when it sold, and to whom. Charities, he says, are a different story.
“If you’re looking at poverty or trafficking or homicide, we don’t have all the data, and we’re not going to,” he says. “That’s why these amazing techniques that the industry people have are great in industry, but they don’t actually generalize to our space very well.”
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.
Lies, Damned Lies and Official Statistics
An Award for Anita Gohdes
Reflections: HRDAG Was Born in Washington
Data ‘hashing’ improves estimate of the number of victims in databases
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.”
Collecting Sensitive Human Rights Data in the Field: A Case Study from Amritsar, India.
Romesh Silva and Jasmine Marwaha. “Collecting Sensitive Human Rights Data in the Field: A Case Study from Amritsar, India.” In JSM Proceedings, Social Statistics Section. Alexandria, VA. © 2011 American Statistical Association. All rights reserved.
The Allegheny Family Screening Tool’s Overestimation of Utility and Risk
Anjana Samant, Noam Shemtov, Kath Xu, Sophie Beiers, Marissa Gerchick, Ana Gutierrez, Aaron Horowitz, Tobi Jegede, Tarak Shah (2023). The Allegheny Family Screening Tool’s Overestimation of Utility and Risk. Logic(s). 13 December, 2023. Issue 20.
Machine learning is being used to uncover the mass graves of Mexico’s missing
“Patrick Ball, HRDAG’s Director of Research and the statistician behind the code, explained that the Random Forest classifier was able to predict with 100% accuracy which counties that would go on to have mass graves found in them in 2014 by using the model against data from 2013. The model also predicted the counties that did not have mass hidden graves found in them, but that show a high likelihood of the possibility. This prediction aspect of the model is the part that holds the most potential for future research.”