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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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.
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.”
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.
Amnesty International Reports Organized Murder Of Detainees In Syrian Prison
Reports of torture and disappearances in Syria are not new. But the Amnesty International report says the magnitude and severity of abuse has “increased drastically” since 2011. Citing the Human Rights Data Analysis Group, the report says “at least 17,723 people were killed in government custody between March 2011 and December 2015, an average of 300 deaths each month.”
Families flock to Syria’s prisons looking for released inmates
According to the Human Rights Data Analysis Group, at least 17,723 people were killed in government custody from the start of the uprising in March 2011 to December 2015 – an average of 300 deaths each month. There are no figures for subsequent years but there is no reason to believe the killings stopped.
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.”
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.
Measures of Fairness for New York City’s Supervised Release Risk Assessment Tool
Kristian Lum and Tarak Shah (2019). Measures of Fairness for New York City's Supervised Release Risk Assessment Tool. Human Rights Data Analysis Group. 1 October 2019. © HRDAG 2019.
Kristian Lum and Tarak Shah (2019). Measures of Fairness for New York City’s Supervised Release Risk Assessment Tool. Human Rights Data Analysis Group. 1 October 2019. © HRDAG 2019.
Update of Iraq and Syria Data in New Paper
This week The Statistical Journal of the IAOS published a new(ish) paper by Megan Price and Patrick Ball. The open-access paper, Selection bias and the statistical patterns of mortality in conflict, is a revisiting and updating of both the Iraq and Syria examples used in an earlier paper, Big Data, Selection Bias, and the Statistical Patterns of Mortality in Conflict, which was published last year inThe SAIS Review of International Affairs (JHU Press, 2014).
HRDAG believes that the concerns highlighted by these examples are important for a wide variety of audiences, including both the foreign policy readers reached by The SAIS Review and the ...
How We Choose Projects
For more than 20 years, HRDAG has been carving out a niche in the international human rights movement. We know what we’re good at and what we’re not qualified to do. We know what quantitative questions we think are important for the community, and we know what we like to do. These preferences guide us as we consider whether to take on a project. We’re scientists, so our priorities will come as no surprise. We like to stick to science (not ideology), avoid advocacy, answer quantifiable questions, and increase our scientific understanding.
While we have no hard-and-fast rules about what projects to take on, we organize our deliberation ...
HRDAG Retreat 2015
I look at the beach and then at the table surrounded by nerds, deep in thought and conversation about Dirichlet priors, matching algorithms, and armed conflicts. This peculiar (in the best way) environment catalyzes a moment of reflection: how did I get here?
Four years ago, as a second-year statistics PhD student, I watched "Guatemala: The Secret Files" on PBS Frontline World. I listened to stories of family members who disappeared without answers or justice. Then the story shifted to the work being done by archivists and data experts at Guatemala's Historic Archive of the National Police. The scientists' pursuit of the truth energized me. I ...
HRDAG To Join the Partnership on AI
HRDAG is joining Partnership on AI to Benefit People and Society (PAI).
Publications
From time to time, we issue our own scientific reports that focus on the statistical aspects of the data analysis we have done in support of our partners. These reports are non-partisan, and they leave the work of advocacy to our partners.
You can search our publications by keyword or by year.