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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).
Counting Civilian Casualties: An Introduction to Recording and Estimating Nonmilitary Deaths in Conflict
ed. by Taylor B. Seybolt, Jay D. Aronson, and Baruch Fischhoff. Oxford University Press. © 2013 Oxford University Press. All rights reserved.
The following four chapters are included:
— Todd Landman and Anita Gohdes (2013). “A Matter of Convenience: Challenges of Non-Random Data in Analyzing Human Rights Violations in Peru and Sierra Leone.”
— Jeff Klingner and Romesh Silva (2013). “Combining Found Data and Surveys to Measure Conflict Mortality.”
— Daniel Manrique-Vallier, Megan E. Price, and Anita Gohdes (2013). “Multiple-Systems Estimation Techniques for Estimating Casualties in Armed Conflict.”
— Jule KrĂĽger, Patrick Ball, Megan Price, and Amelia Hoover Green (2013). “It Doesn’t Add Up: Methodological and Policy Implications of Conflicting Casualty Data.”
Even if there’s a ceasefire, thousands of deaths projected in Gaza over next 6 months
In this NPR story, HRDAG’s Patrick Ball comments on first-of-its-kind projections.
Machine learning is being used to uncover the mass graves of Mexico’s missing
“Patrick Ball, HRDAG’s Director of Research and the statistician behind the code, explained that the Random Forest classifier was able to predict with 100% accuracy which counties that would go on to have mass graves found in them in 2014 by using the model against data from 2013. The model also predicted the counties that did not have mass hidden graves found in them, but that show a high likelihood of the possibility. This prediction aspect of the model is the part that holds the most potential for future research.”
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.
The Truth of Truth Commissions: Comparative Lessons from Haiti, South Africa, and Guatemala.
Audrey Chapman and Patrick Ball. “The Truth of Truth Commissions: Comparative Lessons from Haiti, South Africa, and Guatemala.” Human Rights Quarterly. 23(4):1-42. 2001