This new report by the United Nations Office of High Commissioner of Human Rights builds on three prior analyses and new statistical analysis by HRDAG on killings in Syria.
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The World According to Artificial Intelligence (Part 2)
The World According to Artificial Intelligence – The Bias in the Machine (Part 2)
Artificial intelligence might be a technological revolution unlike any other, transforming our homes, our work, our lives; but for many – the poor, minority groups, the people deemed to be expendable – their picture remains the same.
Patrick Ball is interviewed: “The question should be, Who bears the cost when a system is wrong?”
Big Data Predictive Analytics Comes to Academic and Nonprofit Institutions to Fuel Innovation
“Revolution Analytics will allow HRDAG to handle bigger data sets and leverage the power of R to accomplish this goal and uncover the truth.” Director of Research Megan Price is quoted
Measuring Elusive Populations with Bayesian Model Averaging for Multiple Systems Estimation: A Case Study on Lethal Violations in Casanare, 1998-2007
Kristian Lum, Megan Price, Tamy Guberek, and Patrick Ball. “Measuring Elusive Populations with Bayesian Model Averaging for Multiple Systems Estimation: A Case Study on Lethal Violations in Casanare, 1998-2007,” Statistics, Politics, and Policy. 1(1) 2010. All rights reserved.
Judges in Habré Trial Cite HRDAG Analysis
Three months after the announcement of the momentous verdict finding former Chadian president Hissène Habré guilty of crimes against humanity, the presiding judges have released the full, written 681-page judgment of the court. Testimony given by HRDAG’s director of research, Patrick Ball, is mentioned at three points in the verdict.
The judges included in their written judgment the HRDAG analysis that the mortality rate in Habré prisons was staggeringly high—much higher than the mortality rate among the population as a whole. Here’s an excerpt from the judgment, page 358 (translated by Google):
The statistical expert, Patrick Ball, ...
HRDAG Retreat 2018
What follows is an elaborate criss-crossing of collaborations—retreat is a time to embrace the productivity that comes with being in the same room.
A Model to Estimate SARS-CoV-2-Positive Americans
We’ve built a model for estimating the true number of positives, using what we have determined to be the most reliable datasets—deaths.
To Count the Uncounted: An Estimation of Lethal Violence in Casanare,
Tamy Guberek, Daniel Guzmán, Megan Price, Kristian Lum and Patrick Ball, “To Count the Uncounted: An Estimation of Lethal Violence in Casanare,” A Report by the Benetech Human Rights Program. 10 February 2010. (Available in Spanish) © 2010 Benetech. Creative Commons BY-NC-SA.
PredPol amplifies racially biased policing
HRDAG associate William Isaac is quoted in this article about how predictive policing algorithms such as PredPol exacerbate the problem of racial bias in policing.
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.”
What we’ll need to find the true COVID-19 death toll
From the article: “Intentionally inconsistent tracking can also influence the final tally, notes Megan Price, a statistician at the Human Rights Data Analysis Group. During the Iraq War, for example, officials worked to conceal mortality or to cherry pick existing data to steer the political narrative. While wars are handled differently from pandemics, Price thinks the COVID-19 data could still be at risk of this kind of manipulation.”
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.