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How we go about estimating casualties in Syria—Part 1
BJS Report on Arrest-Related Deaths: True Number Likely Much Greater
Rise of the racist robots – how AI is learning all our worst impulses
“If you’re not careful, you risk automating the exact same biases these programs are supposed to eliminate,” says Kristian Lum, the lead statistician at the San Francisco-based, non-profit Human Rights Data Analysis Group (HRDAG). Last year, Lum and a co-author showed that PredPol, a program for police departments that predicts hotspots where future crime might occur, could potentially get stuck in a feedback loop of over-policing majority black and brown neighbourhoods. The program was “learning” from previous crime reports. For Samuel Sinyangwe, a justice activist and policy researcher, this kind of approach is “especially nefarious” because police can say: “We’re not being biased, we’re just doing what the math tells us.” And the public perception might be that the algorithms are impartial.
How Predictive Policing Reinforces Bias
Primer to Inform Discussions about Bail Reform
RustConf 2019, and systems programming as a data scientist
How Pretrial Risk Assessment Tools Perpetuate Unfairness
.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
Liberia
Multiple Systems Estimation: Does it Really Work?
Justice by the Numbers
Wilkerson was speaking at the inaugural Conference on Fairness, Accountability, and Transparency, a gathering of academics and policymakers working to make the algorithms that govern growing swaths of our lives more just. The woman who’d invited him there was Kristian Lum, the 34-year-old lead statistician at the Human Rights Data Analysis Group, a San Francisco-based non-profit that has spent more than two decades applying advanced statistical models to expose human rights violations around the world. For the past three years, Lum has deployed those methods to tackle an issue closer to home: the growing use of machine learning tools in America’s criminal justice system.
Measuring Elusive Populations with Bayesian Model Averaging for Multiple Systems Estimation: A Case Study on Lethal Violations in Casanare, 1998-2007
Kristian Lum, Megan Price, Tamy Guberek, and Patrick Ball. “Measuring Elusive Populations with Bayesian Model Averaging for Multiple Systems Estimation: A Case Study on Lethal Violations in Casanare, 1998-2007,” Statistics, Politics, and Policy. 1(1) 2010. All rights reserved.
Learning the Hard Way at the ICTY: Statistical Evidence of Human Rights Violations in an Adversarial Information Environment.
Amelia Hoover Green. In Collective Violence and International Criminal Justice: An Interdisciplinary Approach, ed. Alette Smeulers, Antwerp, Belgium. © 2010 Intersentia. All rights reserved. [Link coming soon]