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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.”
The causal impact of bail on case outcomes for indigent defendants in New York City
Kristian Lum, Erwin Ma and Mike Baiocchi (2017). The causal impact of bail on case outcomes for indigent defendants in New York City. Observational Studies 3 (2017) 39-64. 31 October 2017. © 2017 Institute of Mathematical Statistics.
Trump’s “extreme-vetting” software will discriminate against immigrants “Under a veneer of objectivity,” say experts
Kristian Lum, lead statistician at the Human Rights Data Analysis Group (and letter signatory), fears that “in order to flag even a small proportion of future terrorists, this tool will likely flag a huge number of people who would never go on to be terrorists,” and that “these ‘false positives’ will be real people who would never have gone on to commit criminal acts but will suffer the consequences of being flagged just the same.”
Theoretical limits of microclustering for record linkage
John E Johndrow, Kristian Lum and D B Dunson (2018). Theoretical limits of microclustering for record linkage. Biometrika. 19 March 2018. © 2018 Oxford University Press. DOI 10.1093/biomet/asy003.
Data ‘hashing’ improves estimate of the number of victims in databases
But while HRDAG’s estimate relied on the painstaking efforts of human workers to carefully weed out potential duplicate records, hashing with statistical estimation proved to be faster, easier and less expensive. The researchers said hashing also had the important advantage of a sharp confidence interval: The range of error is plus or minus 1,772, or less than 1 percent of the total number of victims.
“The big win from this method is that we can quickly calculate the probable number of unique elements in a dataset with many duplicates,” said Patrick Ball, HRDAG’s director of research. “We can do a lot with this estimate.”
Unbiased algorithms can still be problematic
“Usually, the thing you’re trying to predict in a lot of these cases is something like rearrest,” Lum said. “So even if we are perfectly able to predict that, we’re still left with the problem that the human or systemic or institutional biases are generating biased arrests. And so, you still have to contextualize even your 100 percent accuracy with is the data really measuring what you think it’s measuring? Is the data itself generated by a fair process?”
HRDAG Director of Research Patrick Ball, in agreement with Lum, argued that it’s perhaps more practical to move it away from bias at the individual level and instead call it bias at the institutional or structural level. If a police department, for example, is convinced it needs to police one neighborhood more than another, it’s not as relevant if that officer is a racist individual, he said.
Preserving Human Rights Data with the Filecoin Network: A Journey into the Decentralized Web with HRDAG
Patrick Ball (2024). Preserving Human Rights Data with the Filecoin Network: A Journey into the Decentralized Web with HRDAG. Filecoin Foundation for the Decentralized Web. 18 April, 2024.
Foundation of Human Rights Statistics in Sierra Leone
Richard Conibere (2004). Foundation of Human Rights Statistics in Sierra Leone (abstr.), Joint Statistical Meetings. Toronto, Canada.
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.
The Demography of Conflict-Related Mortality in Timor-Leste (1974-1999): Empirical Quantitative Measurement of Civilian Killings, Disappearances & Famine-Related Deaths
Romesh Silva and Patrick Ball. “The Demography of Conflict-Related Mortality in Timor-Leste (1974-1999): Empirical Quantitative Measurement of Civilian Killings, Disappearances & Famine-Related Deaths” In Statistical Methods for Human Rights, J. Asher, D. Banks and F. Scheuren, eds., Springer (New York) (2007)
Truth and Reconciliation Commission of Perú, Final Report – General Conclusions.
Truth and Reconciliation Commission of Perú, Final Report – General Conclusions. Comisión de la verdad y reconciliación, 2003.
Press Release, Timor-Leste, November 2006
Applications of Multiple Systems Estimation in Human Rights Research
Lum, Kristian, Megan Emily Price, and David Banks. 2013. The American Statistician 67, no. 4: 191-200. doi: 10.1080/00031305.2013.821093. © 2013 The American Statistician. All rights reserved. [free eprint may be available].
When It Comes to Human Rights, There Are No Online Security Shortcuts
Patrick Ball. When It Comes to Human Rights, There Are No Online Security Shortcuts, Wired op-ed, August 10, 2012. Wired.com © 2013 Condé Nast. All rights reserved.
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).
“El reto de la estadística es encontrar lo escondido”: experto en manejo de datos sobre el conflicto
In this interview with Colombian newspaper El Espectador, Patrick Ball is quoted as saying “la gente que no conoce de álgebra nunca debería hacer estadísticas” (people who don’t know algebra should never do statistics).