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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.”
HRDAG’s Year End Review: 2018
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
Tech Note – using LLMs for structured info extraction
Tech Corner
Frequently Asked Questions
Reflections on Data Science for Real-World Problems
Multiple Systems Estimation: Stratification and Estimation
500 Tamils forcibly disappeared in three days, after surrendering to army in 2009
A new study has estimated that over 500 Tamils were forcibly disappeared in just three days, after surrendering to the Sri Lankan army in May 2009.
The study, carried out by the Human Rights Data Analysis Group and the International Truth and Justice Project, based on compiled lists which identify those who were known to have surrendered, estimated that 503 people had been forcibly disappeared between the 17th– 19th of May 2009.
Epidemiology has theories. We should study them.
Beka Steorts Named MIT Under-35 Innovator
Tchad Foire Aux Quesions
Corrigendum: Killings and Refugee Flow in Kosovo, March–June, 1999 (A report to ICTY)
Responses to questions from ICTY office Corrigendum: Killings and Refugee Flow in Kosovo, March–June, 1999 (A report to ICTY) . © 2002 AAAS and ABA CEELI.
Analizando los patrones de violencia en Colombia con más de 100 bases de datos
Violence in Blue: The 2020 Update
Reflections: Pivotal Moments in Freetown
PredPol amplifies racially biased policing
HRDAG associate William Isaac is quoted in this article about how predictive policing algorithms such as PredPol exacerbate the problem of racial bias in policing.
Reflections: Challenging Tasks and Meticulous Defenders
Film: Solving for X
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.