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.

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.

Video available. La organización Dejusticia, en alianza con una institución estadounidense, asegura que los crímenes van en aumento y existe un subregistro. “Aumentó la violencia letal contra líderes sociales en 2016 y 2017 en al menos 10%”, asegura Valentina Rozo, investigadora de Dejusticia.

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.

“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.”

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, 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.”

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.

The American Statistical Association’s 2018 Karl E. Peace Award for Outstanding Statistical Contributions for the Betterment of Society recently recognized the work of leading human rights mathematician Patrick Ball of the Human Rights Data Analysis Group (HRDAG). The award is presented annually to statisticians whose exemplary statistical research is matched by the impact their work has had on the lives of people.

Established by the family of Karl E. Peace in honor of his work for the good of society, the award—announced at the Joint Statistical Meetings—is bestowed upon distinguished individual(s) who have made substantial contributions to the statistical profession, contributions that have led in direct ways to improving the human condition. Recipients will have demonstrated through their accomplishments their commitment to service for the greater good.”

This year, Ball became the 10th recipient of the award. Read more …

Patrick Ball, estadístico norteamericano, colabora con el Programa de Derechos Humanos de la Universidad Iberoamericana en una investigación sobre fosas clandestinas.

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.

“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.”

“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.”

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.”

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.”

“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.

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.

On the heels of the Women in Data Science conference, HRDAG executive director Megan Price says, “I think creativity and communication are probably the two most important skills for a data scientist to have these days.”