“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.”

From the article: Esta entidad, que existe desde 1991, es liderada por su fundador, Patrick Ball, un científico que acumula una experiencia de más de 25 años realizando análisis cuantitativos en los lugares y en las situaciones más convulsos del planeta. Sobre su colaboración con el proyecto del predictor de fosas clandestinas en México, único en el mundo, Ball afirmó en entrevista:

“Cuando hablamos de crímenes de lesa humanidad estamos hablando de instituciones, de organizaciones grandes, cometiendo miles o centenares de miles  de violaciones a víctimas distribuidas sobre una geografía enorme. Para entender los patrones en esas violaciones, la estadística puede brindar una mirada sobre quiénes son los responsables materiales e intelectuales, quiénes son las víctimas y dónde o cuándo pasaron esas violaciones. Pero la estadística no es contabilidad, pues no estamos hablando solamente de las violaciones que podemos ver, sino que también debemos calcular las violaciones no observadas, las escondidas, invisibles, para incluir en nuestro análisis la totalidad de las violaciones”.

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.

The International Truth and Justice Project and HRDAG have published a report on 500 Tamils who disappeared while in Army custody in Sri Lanka in 2009.

The report is titled “How many people disappeared on 17-19 May 2009 in Sri Lanka?” and Patrick Ball, director of research at HRDAG, is the lead author.

“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.

But while HRDAG’s estimate relied on the painstaking efforts of human workers to carefully weed out potential duplicate records, hashing with statistical estimation proved to be faster, easier and less expensive. The researchers said hashing also had the important advantage of a sharp confidence interval: The range of error is plus or minus 1,772, or less than 1 percent of the total number of victims.

“The big win from this method is that we can quickly calculate the probable number of unique elements in a dataset with many duplicates,” said Patrick Ball, HRDAG’s director of research. “We can do a lot with this estimate.”