“Every kind of classification system – human or machine – has several kinds of errors it might make,” [Patrick Ball] says. “To frame that in a machine learning context, what kind of error do we want the machine to make?” HRDAG’s work on predictive policing shows that “predictive policing” finds patterns in police records, not patterns in occurrence of crime.

Coverage of Megan Price at the Women in Data Science Conference held at Stanford University. “Price discussed her organization’s behind-the-scenes work to collect and analyze data on the ground for human rights advocacy organizations. HRDAG partners with a wide variety of human rights organizations, including local grassroots non-governmental groups and—most notably—multiple branches of the United Nations.”

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.”

“The model uses obvious predictor variables, Ball says, such as whether or not a drug lab has been busted in that county, or if the county borders the United States, or the ocean, but also includes less-obvious predictor variables such as the percentage of the county that is mountainous, the presence of highways, and the academic results of primary and secondary school students in the county.”

An excerpt: The “It breaks the human” report released by the human rights group Amnesty International highlights new statistics from the Human Rights Data Analysis Group, or HRDAG, an organization that uses scientific approaches to analyze human rights violations.

Kristian Lum discusses the challenges of getting accurate data from conflict zones, as well as her concerns about predictive policing if law enforcement gets it wrong.

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.”

El modelo “puede distinguir entre los municipios en que vamos a encontrar fosas clandestinas, y en los que es improbable que vayamos a encontrar estas fosas”, explicó Patrick Ball, estadístico estadounidense que colabora con el Programa de Derechos Humanos de la Universidad Iberoamericana de la Ciudad de México.

Por ello, Human Rights Data Analysis Group (HRDAG), el Programa de Derechos Humanos de la Universidad Iberoamericana (UIA) y Data Cívica, realizan un análisis estadístico construido a partir de una variable en la que se identifican fosas clandestinas a partir de búsquedas automatizadas en medios locales y nacionales, y usando datos geográficos y sociodemográficos.

As executive director of the Human Rights Data Analysis Group, Megan Price uses statistics to shine the light on human rights abuses.

Kristian Lum, lead statistician at the Human Rights Data Analysis Group (and letter signatory), fears that “in order to flag even a small proportion of future terrorists, this tool will likely flag a huge number of people who would never go on to be terrorists,” and that “these ‘false positives’ will be real people who would never have gone on to commit criminal acts but will suffer the consequences of being flagged just the same.”

In Responsible Data Reflection Story #7—from the Responsible Data Forum—work by HRDAG affiliates Anita Gohdes and Brian Root is cited extensively to make the point about how quantitative data are the result of numerous subjective human decisions. An excerpt: “The Human Rights Data Analysis Group are pioneering the way in collecting and analysing figures of killings in conflict in a responsible way, using multiple systems estimation.”

In this free, downloadable report, Mike Barlow of O’Reilly Media cites several examples of how data and the work of data scientists have made a measurable impact on organizations such as DataKind, a group that connects socially minded data scientists with organizations working to address critical humanitarian issues. HRDAG—and executive director Megan Price—is one of the first organizations whose work is mentioned.

Cory Doctorow of Boing Boing writes about HRDAG director of research Patrick Ball’s article “Violence in Blue,” published March 4 in Granta. From the post: “In a must-read article in Granta, Ball explains the fundamentals of statistical estimation, and then applies these techniques to US police killings, merging data-sets from the police and the press to arrive at an estimate of the knowable US police homicides (about 1,250/year) and the true total (about 1,500/year). That means that of all the killings by strangers in the USA, one third are committed by the police.”

HRDAG director of research Patrick Ball is quoted in this Nature article about how body counts are a crude measure of the war’s impact and more reliable estimates will take time to compile.

HRDAG is mentioned in the “child welfare (sometimes called “family policing”)” section: At least 72,000 low-income children are exposed to AI-related decision-making through government child welfare agencies’ use of AI to determine if they are likely to be neglected. As a result, these children experience heightened risk of being separated from their parents and placed in foster care.