228 results for search: www.xn--299aqd11rg1lb34as7a.net/feed/copyright
BJS Report on Arrest-Related Deaths: True Number Likely Much Greater
How Predictive Policing Reinforces Bias
Herb Spirer, 1925 – 2018
How Pretrial Risk Assessment Tools Perpetuate Unfairness
Primer to Inform Discussions about Bail Reform
.Rproj Considered Harmful
Learning a Modular, Auditable and Reproducible Workflow
Identifiers of Detained Children Have Implications for Data Security and Estimation
How Data Analysis Confirmed the Bias in a Family Screening Tool
Can the Armed Conflict Become Part of Colombia’s History?
RustConf 2019, and systems programming as a data scientist
Calculating US police killings using methodologies from war-crimes trials
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.”
Improving the estimate of U.S. police killings
Cory Doctorow of Boing Boing writes about HRDAG executive director Patrick Ball and his contribution to Carl Bialik’s article about the recently released Bureau of Justice Statistics report on the number of annual police killings, both reported and unreported, in 538 Politics.
Predictive policing tools send cops to poor/black neighborhoods
In this post, Cory Doctorow writes about the Significance article co-authored by Kristian Lum and William Isaac.
Megan Price Elected Board Member of Tor Project
HRDAG Names New Board Members Julie Broome and Frank Schulenburg
Weapons of Math Destruction
Weapons of Math Destruction: invisible, ubiquitous algorithms are ruining millions of lives. Excerpt:
As Patrick once explained to me, you can train an algorithm to predict someone’s height from their weight, but if your whole training set comes from a grade three class, and anyone who’s self-conscious about their weight is allowed to skip the exercise, your model will predict that most people are about four feet tall. The problem isn’t the algorithm, it’s the training data and the lack of correction when the model produces erroneous conclusions.