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.

In the last three days of the Sri Lankan civil war, as thousands of people surrendered to government authorities, hundreds of people were put on buses driven by Army officers. Many were never seen again.

In a report released today (see here), the International Truth and Justice Project for Sri Lanka and the Human Rights Data Analysis Group showed that over 500 people were disappeared on only three days — 17, 18, and 19 May.

The University of Michigan College of Literature, Science and the Arts profiled Patrick Ball in its fall 2016 issue of the alumni magazine. Here’s an excerpt:

Ball believes doing this laborious, difficult work makes the world a more just place because it leads to accountability.

“My part is a specific, narrow piece, which just happens to fit with the skills I have,” he says. “I don’t think that what we do is in any way the best or most important part of human rights activism. Sometimes, we are just a footnote—but we are a really good footnote.”

In this post, Cory Doctorow writes about the Significance article co-authored by Kristian Lum and William Isaac.

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.”

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.”

Kristian Lum: “The historical over-policing of minority communities has led to a disproportionate number of crimes being recorded by the police in those locations. Historical over-policing is then passed through the algorithm to justify the over-policing of those communities.”

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.”

“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.

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.”

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.”

“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.