The tension started in the witness room. “You could feel the stress rolling off the walls in there,” Patrick Ball remembers. “I can remember realizing that this is why lawyers wear sport coats – you can’t see all the sweat on their arms and back.” He was, you could say, a little nervous to be cross-examined by Slobodan Milosevic.

“In 2016, two researchers, the statistician Kristian Lum and the political scientist William Isaac, set out to measure the bias in predictive policing algorithms. They chose as their example a program called PredPol.  … Lum and Isaac faced a conundrum: if official data on crimes is biased, how can you test a crime prediction model? To solve this technique, they turned to a technique used in statistics and machine learning called the synthetic population.”

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.”

“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.

Excerpt from the article: The estimate is based on reports from four organizations investigating deaths in Syria from March 15, 2011, to December, 31, 2015. From those cases, the Human Rights Data Analysis Group identified 12,270 cases with sufficient information to confirm the person was killed in detention. Using a statistical method to estimate how many victims they do not yet know about, the group came up with 17,723 cases.

Earlier this year, the Canadian Tamil Congress connected with HRDAG to bring its campaign to Toronto’s annual Tamil Fest, one of the largest gatherings of Canada’s Sri Lankan diaspora.

Ravichandradeva, along with a few other volunteers, spent the weekend speaking with festival-goers in Scarborough about the project and encouraging them to come forward with information about deceased or missing loved ones and friends.

“The idea is to collect thorough, scientifically rigorous numbers on the total casualties in the war and present them as a non-partisan, independent organization,” said Michelle Dukich, a data consultant with HRDAG.

When Patrick Ball was introduced to Ibero’s database, the director of research at the Human Rights Data Analysis Group in San Francisco, California, saw an opportunity to turn the data into a predictive model. Ball, who has used similar models to document human rights violations from Syria to Guatemala, soon invited Data Cívica, a Mexico City–based nonprofit that creates tools for analyzing data, to join the project.

Henrik Urdal has released his final Nobel Shortlist for 2022, and HRDAG is included on it, alongside Sviatlana Tsikhanouskaya and Alexei Navalny, and others. The list highlights pro-democracy efforts, multilateral cooperation, combating religious extremism and intolerance, and the value that research and knowledge can have for promoting peace.

HRDAG director of research Patrick Ball is quoted in this New York Times article about a paper that models death tolls in Gaza.

International human rights work attracts activists and lawyers, diplomats and retired politicians. One of the most admired figures in the field, however, is a ponytailed statistics guru from Silicon Valley named Patrick Ball, who has spent nearly two decades fashioning a career for himself at the intersection of mathematics and murder. You could call him a forensic humanitarian.

HRDAG analyst William Isaac is quoted in this article about so-called crime prediction. “They’re not predicting the future. What they’re actually predicting is where the next recorded police observations are going to occur.”

Eric Sears, a director at the MacArthur Foundation who leads the grantmaker’s Technology in the Public Interest program, worked at Human Rights First and Amnesty International before joining MacArthur, and has been following HRDAG’s work for years. … One of HRDAG’s strengths is the long relationships it maintains with partners around the globe. “HRDAG is notable in that it really develops deep relationships and partnerships and trust with organizations and actors in different parts of the world,” Sears said. “I think they’re unique in the sense that they don’t parachute into a situation and do a project and leave. They tend to stick with organizations and with issues over the long term, and continually help build cases around evidence and documentation to ensure that when the day comes, when accountability is possible, the facts and the evidence are there.”

Kristian Lum’s work on the HRDAG Policing Project is referred to here: “In fact, Lum argues, it’s not clear how well this model worked at depicting the situation in Oakland. Those data on drug crimes were biased, she now reports. The problem was not deliberate, she says. Rather, data collectors just missed some criminals and crime sites. So data on them never made it into her model.”

Kristian Lum is focusing on artificial intelligence and the controversial use of predictive policing and sentencing programs.

What’s the relationship between statistics and AI and machine learning?

AI seems to be a sort of catchall for predictive modeling and computer modeling. There was this great tweet that said something like, “It’s AI when you’re trying to raise money, ML when you’re trying to hire developers, and statistics when you’re actually doing it.” I thought that was pretty accurate.