Work by HRDAG researchers Kristian Lum and William Isaac is cited in this article about the Policing Project: “While this bias knows no color or socioeconomic class, Lum and her HRDAG colleague William Isaac demonstrate that it can lead to policing that unfairly targets minorities and those living in poorer neighborhoods.”

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.”

Ball analyzed the data reporters had collected from a variety of sources – including on-the-ground interviews, police records, and human rights groups – and used a statistical technique called multiple systems estimation to roughly calculate the number of unreported deaths in three areas of the capital city Manila.

The team discovered that the number of drug-related killings was much higher than police had reported. The journalists, who published their findings last month in The Atlantic, documented 2,320 drug-linked killings over an 18-month period, approximately 1,400 more than the official number. Ball’s statistical analysis, which estimated the number of killings the reporters hadn’t heard about, found that close to 3,000 people could have been killed – more than three times the police figure.

Ball said there are both moral and technical reasons for making sure everyone who has been killed in mass violence is counted.

“The moral reason is because everyone who has been murdered should be remembered,” he said. “A terrible thing happened to them and we have an obligation as a society to justice and to dignity to remember them.”

A new study has estimated that over 500 Tamils were forcibly disappeared in just three days, after surrendering to the Sri Lankan army in May 2009.

The study, carried out by the Human Rights Data Analysis Group and the International Truth and Justice Project, based on compiled lists which identify those who were known to have surrendered, estimated that 503 people had been forcibly disappeared between the 17^th– 19^th of May 2009.

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.

“Kristian Lum… organized a workshop together with Elizabeth Bender, a staff attorney for the NY Legal Aid Society and former public defender, and Terrence Wilkerson, an innocent man who had been arrested and could not afford bail. Together, they shared first hand experience about the obstacles and inefficiencies that occur in the legal system, providing valuable context to the debate around COMPAS.”

Contrario a lo que se puede pensar, los datos oficiales sobre líderes sociales asesinados no necesariamente corresponden a la realidad y podría haber mucha mayor victimización en las regiones golpeadas por este flagelo, según el más reciente informe del Centro de Estudios de Justicia, Derecho y Sociedad (Dejusticia) en colaboración con el Human Rights Data Analysis Group.

Una investigación de Dejusticia y Human Rights Data Analysis Group concluyó que hay un subconteo en los asesinatos de líderes sociales en Colombia. Es decir, que el aumento de estos crímenes en 2016 y 2017 podría ser incluso mayor al reportado por las organizaciones y por las cifras oficiales.

Una investigación de Dejusticia y Human Rights Data Analysis Group  asegura que en Colombia hay un subregistro de los asesinatos de líderes sociales que se han perpetrado en Colombia. Al analizar las diferentes cifras de homicidios que han publicado diversas organizaciones desde 2016, se llegó a la conclusión que la problemática es mayor de lo que se cree.

The Sri Lankan army must explain to the families of the disappeared and missing what happened to an estimated 500 Tamils who disappeared in their custody at the war end on/around 18 May 2009, said two international NGOs who have been collating and analysing lists of names.

Sri Lanka has one of the largest numbers in the world of enforced disappearances but these 500 represent the largest number of disappearances all in one place and time in the country. For a detailed account of the process of estimating the 500 please see: “How many people disappeared on 17-19 May 2009 in Sri Lanka?” .

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.

“If you’re not careful, you risk automating the exact same biases these programs are supposed to eliminate,” says Kristian Lum, the lead statistician at the San Francisco-based, non-profit Human Rights Data Analysis Group (HRDAG). Last year, Lum and a co-author showed that PredPol, a program for police departments that predicts hotspots where future crime might occur, could potentially get stuck in a feedback loop of over-policing majority black and brown neighbourhoods. The program was “learning” from previous crime reports. For Samuel Sinyangwe, a justice activist and policy researcher, this kind of approach is “especially nefarious” because police can say: “We’re not being biased, we’re just doing what the math tells us.” And the public perception might be that the algorithms are impartial.

Kristian Lum, the lead statistician at the Human Rights Data Analysis Group, and an expert on algorithmic bias, said she hoped Stanford’s stumble made the institution think more deeply about representation.

“This type of oversight makes me worried that their stated commitment to the other important values and goals – like taking seriously creating AI to serve the ‘collective needs of humanity’ – is also empty PR spin and this will be nothing more than a vanity project for those attached to it,” she wrote in an email.