“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.”

In her work on statistical issues in criminal justice, Lum has studied uses of predictive policing—machine learning models to predict who will commit future crime or where it will occur. In her work, she has demonstrated that if the training data encodes historical patterns of racially disparate enforcement, predictions from software trained with this data will reinforce and—in some cases—amplify this bias. She also currently works on statistical issues related to criminal “risk assessment” models used to inform judicial decision-making. As part of this thread, she has developed statistical methods for removing sensitive information from training data, guaranteeing “fair” predictions with respect to sensitive variables such as race and gender. Lum is active in the fairness, accountability, and transparency (FAT) community and serves on the steering committee of FAT, a conference that brings together researchers and practitioners interested in fairness, accountability, and transparency in socio-technical systems.

“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.”

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.”

HRDAG contributed to work by the ACLU showing that a predictive tool used to guide responses to alleged child neglect may forever flag parents with disabilities. “These predictors have the effect of casting permanent suspicion and offer no means of recourse for families marked by these indicators,” according to the analysis from researchers at the ACLU and the nonprofit Human Rights Data Analysis Group. “They are forever seen as riskier to their children.”

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.”

Executive director Megan Price is interviewed in The New York Times’ Sunday Review, as part of a series known as “Download,” which features a biosketch of “Influencers and their interests.”

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.”

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.

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.”

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.”

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.

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.

Researchers from Rice University and Duke University are using the tools of statistics and data science in collaboration with Human Rights Data Analysis Group (HRDAG) to accurately and efficiently estimate the number of identified victims killed in the Syrian civil war.

…

Using records from four databases of people killed in the Syrian war, Chen, Duke statistician and machine learning expert Rebecca Steorts and Rice computer scientist Anshumali Shrivastava estimated there were 191,874 unique individuals documented from March 2011 to April 2014. That’s very close to the estimate of 191,369 compiled in 2014 by HRDAG, a nonprofit that helps build scientifically defensible, evidence-based arguments of human rights violations.