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HRDAG Retreat 2022
Reality and risk: A refutation of S. Rendón’s analysis of the Peruvian Truth and Reconciliation Commission’s conflict mortality study
Daniel Manrique-Vallier and Patrick Ball (2019). Reality and risk: A refutation of S. Rendón’s analysis of the Peruvian Truth and Reconciliation Commission’s conflict mortality study. Research & Politics, 22 March 2019. © Sage Journals. https://doi.org/10.1177/2053168019835628
The impact of overbooking on a pre-trial risk assessment tool
Kristian Lum, Chesa Boudin and Megan Price (2020). The impact of overbooking on a pre-trial risk assessment tool. FAT* ’20: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. January 2020. Pages 482–491. https://doi.org/10.1145/3351095.3372846 ©ACM, Inc., 2020.
Low-risk population size estimates in the presence of capture heterogeneity
James Johndrow, Kristian Lum and Daniel Manrique-Vallier (2019). Low-risk population size estimates in the presence of capture heterogeneity. Biometrika, asy065, 22 January 2019. © 2019 Biometrika Trust. https://doi.org/10.1093/biomet/asy065
Beautiful game, ugly truth?
Megan Price (2022). Beautiful game, ugly truth? Significance, 19: 18-21. December 2022. © The Royal Statistical Society. https://doi.org/10.1111/1740-9713.01702
Capture-Recapture for Casualty Estimation and Beyond: Recent Advances and Research Directions
Manrique-Vallier, D., Ball, P., Sadinle, M. (2022). Capture-Recapture for Casualty Estimation and Beyond: Recent Advances and Research Directions. In: Carriquiry, A.L., Tanur, J.M., Eddy, W.F. (eds) Statistics in the Public Interest. Springer Series in the Data Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-75460-0_2
Innocence Discovery Lab – Harnessing Large Language Models to Surface Data Buried in Wrongful Conviction Case Documents
Ayyub Ibrahim, Huy Dao, and Tarak Shah (2024). “Innocence Discovery Lab – Harnessing Large Language Models to Surface Data Buried in Wrongful Conviction Case Documents.” The Wrongful Conviction Law Review 5 (1):103-25. https://doi.org/10.29173/wclawr112. 31 May, 2024. Copyright (c) 2024 Ayyub Ibrahim, Huy Dao, Tarak Shah. Creative Commons Attribution 4.0 International License.
The Use of Unstructured Data to Study Police Use of Force
Tarak Shah, Cristian Allen, Ayyub Ibrahim, Harlan Kefalas, and Bavo Stevens (2024). The Use of Unstructured Data to Study Police Use of Force. 5 December, 2024. CHANCE, 37(4), 18–23. https://doi.org/10.1080/09332480.2024.2434437
Layers of Bias: A Unified Approach for Understanding Problems With Risk Assessment
Laurel Eckhouse, Kristian Lum, Cynthia Conti-Cook and Julie Ciccolini (2018). Layers of Bias: A Unified Approach for Understanding Problems With Risk Assessment. Criminal Justice and Behavior. November 23, 2018. © 2018 Sage Journals. All rights reserved. https://doi.org/10.1177/0093854818811379
verdata: An R package for analyzing data from the Truth Commission in Colombia
Maria Gargiulo, María Julia Durán, Paula Andrea Amado, and Patrick Ball (2024). verdata: An R package for analyzing data from the Truth Commission in Colombia. The Journal of Open Source Software. 6 January, 2024. 9(93), 5844, https://doi.org/10.21105/joss.05844. Creative Commons Attribution 4.0 International License.
Stephen Fienberg 1942-2016
Using Data and Statistics to Bring Down Dictators
In this story, Guerrini discusses the impact of HRDAG’s work in Guatemala, especially the trials of General José Efraín Ríos Montt and Colonel Héctor Bol de la Cruz, as well as work in El Salvador, Syria, Kosovo, and Timor-Leste. Multiple systems estimation and the perils of using raw data to draw conclusions are also addressed.
Megan Price and Patrick Ball are quoted, especially in regard to how to use raw data.
“From our perspective,” Price says, “the solution to that is both to stay very close to the data, to be very conservative in your interpretation of it and to be very clear about where the data came from, how it was collected, what its limitations might be, and to a certain extent to be skeptical about it, to ask yourself questions like, ‘What is missing from this data?’ and ‘How might that missing information change these conclusions that I’m trying to draw?’”
Core Concepts
HRDAG Retreat 2015
Using Data and Statistics to Bring Down Dictators
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.
The Case Against a Golden Key
Patrick Ball (2016). The case against a golden key. Foreign Affairs. September 14, 2016. ©2016 Council on Foreign Relations, Inc. All Rights Reserved.
Counting The Dead: How Statistics Can Find Unreported Killings
Ball analyzed the data reporters had collected from a variety of sources – including on-the-ground interviews, police records, and human rights groups – and used a statistical technique called multiple systems estimation to roughly calculate the number of unreported deaths in three areas of the capital city Manila.
The team discovered that the number of drug-related killings was much higher than police had reported. The journalists, who published their findings last month in The Atlantic, documented 2,320 drug-linked killings over an 18-month period, approximately 1,400 more than the official number. Ball’s statistical analysis, which estimated the number of killings the reporters hadn’t heard about, found that close to 3,000 people could have been killed – more than three times the police figure.
Ball said there are both moral and technical reasons for making sure everyone who has been killed in mass violence is counted.
“The moral reason is because everyone who has been murdered should be remembered,” he said. “A terrible thing happened to them and we have an obligation as a society to justice and to dignity to remember them.”