Ball, a statistician, has spent the last two decades finding ways to make the silence speak. He helped pioneer the use of formal statistical modeling, and, later, machine learning—tools more often used for e-commerce or digital marketing—to measure human rights violations that weren’t recorded. In Guatemala, his analysis helped convict former dictator General Efraín Ríos Montt of genocide in 2013. It was the first time a former head of state was found guilty of the crime in his own country.

“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.”

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.”

One of the researchers, a Michigan State PhD candidate named William Isaac, had not previously heard of New Orleans’ partnership with Palantir, but he recognized the data-mapping model at the heart of the program. “I think the data they’re using, there are serious questions about its predictive power. We’ve seen very little about its ability to forecast violent crime,” Isaac said.

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.

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.

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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.

“I was always a math nerd. My mother has a polaroid of me in the fourth grade with my science fair project … . It was the history of mathematics. In college, I was a math major for a year and then switched to statistics.

I always wanted to work in social justice. I was raised by hippies, went to protests when I was young. I always felt I had an obligation to make the world a little bit better.”