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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.
How do epidemiologists know how many people will get Covid-19?
Patrick Ball (2020). How do epidemiologists know how many people will get Covid-19? Significance. 09 April 2020. © 2020 The Royal Statistical Society.
To predict and serve?
Kristian Lum and William Isaac (2016). To predict and serve? Significance. October 10, 2016. © 2016 The Royal Statistical Society.
Why Just Counting the Dead in Syria Won’t Bring Them Justice
Patrick Ball (2016). Why Just Counting the Dead in Syria Won’t Bring Them Justice. Foreign Policy. October 19, 2016. © 2016 Foreign Policy.
La importancia de la estadística
Patrick Ball (2018). La importancia de la estadística. Ibero. La revista de la universidad Iberoamericana. August-September 2018. © 2018 Universidad Iberoamericana Ciudad de México. Pp. 50-51.
How much faith can we place in coronavirus antibody tests?
Megan Price, Morgan Agnew, and David Peters (2020). How much faith can we place in coronavirus antibody tests? Granta. 28 April 2020. © Granta Publications 2020.
Cuentas y mediciones de la criminalidad y de la violencia
Exploración y análisis de los datas para comprender la realidad. Patrick Ball y Michael Reed Hurtado. 2015. Forensis 16, no. 1 (July): 529-545. © 2015 Instituto Nacional de Medicina Legal y Ciencias Forenses (República de Colombia).
Datasets available for research
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.
Primer to Inform Discussions about Bail Reform
Learning a Modular, Auditable and Reproducible Workflow
Hunting for Mexico’s mass graves with machine learning
“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.”
Sierra Leone
The Day We Fight Back
First Things First: Assessing Data Quality Before Model Quality.
Anita Gohdes and Megan Price (2013). Journal of Conflict Resolution, Volume 57 Issue 6 December 2013. © 2013 Journal of Conflict Resolution. All rights reserved. Reprinted with permission of SAGE. [online abstract]DOI: 10.1177/0022002712459708.
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.