4 results for month: 10/2016
Training with HRDAG: Rules for Organizing Data and More
I had the pleasure of working with Patrick Ball at the HRDAG office in San Francisco for a week during summer 2016. I knew Patrick from two workshops he previously hosted at the University of Washington’s Centre for Human Rights (UWCHR). The workshops were indispensable to us at UWCHR as we worked to publish a number of datasets on human rights violations during the El Salvador Civil War. The training was all the more helpful because the HRDAG team was so familiar with the data. As part of an impressive career which took him from Ethiopia and Kosovo to Haiti and El Salvador among others, Patrick himself had worked on gathering and analysing ...
Using MSE to Estimate Unobserved Events
At HRDAG, we worry about what we don't know. Specifically, we worry about how we can use statistical techniques to estimate homicides that are not observed by human rights groups. Based on what we've seen studying many conflicts over the last 25 years, what we don't know is often quite different from what we do know.
The technique we use most often to estimate what we don't know is called "multiple systems estimation." In this medium-technical post, I explain how to organize data and use three R packages to estimate unobserved events.
Click here for Computing Multiple Systems Estimation in R.
HRDAG Names New Board Members Julie Broome and Frank Schulenburg
We are pleased to announce that HRDAG will be supported by two additions to our Advisory Board, Julie Broome and Frank Schulenburg.
We’ve worked with Julie for many years, getting to know her when she was Director of Programmes at The Sigrid Rausing Trust. She is now the Director of London-based Ariadne, a network of European funders and philanthropists. She worked at the Trust for seven years, most notably Head of Human Rights, before becoming Director of Programmes in 2014. Before joining the Trust she was Programme Director at the CEELI Institute in Prague, where she was responsible for conducting rule of law-related trainings for judges and ...
Predictive Policing Reinforces Police Bias
Issues surrounding policing in the United States are at the forefront of our national attention. Among these is the use of “predictive policing,” which is the application of statistical or machine learning models to police data, with the goal of predicting where or by whom crime will be committed in the future. Today Significance magazine published an article on this topic that I co-authored with William Isaac. Significance has kindly made this article open access (free!) for all of October. In the article we demonstrate the mechanism by which the use of predictive policing software may amplify the biases that already pervade our criminal ...