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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.
Using MSE to Estimate Unobserved Events
Lies, Damned Lies and Official Statistics
Welcoming Our 2019 Human Rights Intern
HRDAG at FAT* 2020: Pre-Trial Risk Assessment Tools
Skoll World Forum 2018
The World According to Artificial Intelligence (Part 2)
The World According to Artificial Intelligence – The Bias in the Machine (Part 2)
Artificial intelligence might be a technological revolution unlike any other, transforming our homes, our work, our lives; but for many – the poor, minority groups, the people deemed to be expendable – their picture remains the same.
Patrick Ball is interviewed: “The question should be, Who bears the cost when a system is wrong?”
Liberian Truth and Reconciliation Commission Data
Policy or Panic? The Flight of Ethnic Albanians from Kosovo, March–May, 1999.
Patrick Ball. Policy or Panic? The Flight of Ethnic Albanians from Kosovo, March–May, 1999. © 2000 American Association for the Advancement of Science, Science and Human Rights Program. [pdf – English][html – English][html – shqip (Albanian)] [html – srpski (Serbian)]
Collecting Sensitive Human Rights Data in the Field: A Case Study from Amritsar, India.
Romesh Silva and Jasmine Marwaha. “Collecting Sensitive Human Rights Data in the Field: A Case Study from Amritsar, India.” In JSM Proceedings, Social Statistics Section. Alexandria, VA. © 2011 American Statistical Association. All rights reserved.
The Allegheny Family Screening Tool’s Overestimation of Utility and Risk
Anjana Samant, Noam Shemtov, Kath Xu, Sophie Beiers, Marissa Gerchick, Ana Gutierrez, Aaron Horowitz, Tobi Jegede, Tarak Shah (2023). The Allegheny Family Screening Tool’s Overestimation of Utility and Risk. Logic(s). 13 December, 2023. Issue 20.
Collaboration between the Colombian Truth Commission, the Special Jurisdiction for Peace, and HRDAG (Dataset)
The Colombian Truth Commission (CEV), the Special Jurisdiction for Peace (JEP), and the Human Rights Data Analysis Group (HRDAG) have worked together to integrate data and calculate statistical estimates of the number of victims of the armed conflict, including homicides, forced disappearances, kidnapping, and the recruitment of child soldiers. Data are available through National Administrative Department of Statistics (DANE), the Truth Commission, and GitHub.
Selection Bias and the Statistical Patterns of Mortality in Conflict.
Megan Price and Patrick Ball. 2015. Statistical Journal of the IAOS 31: 263–272. doi: 10.3233/SJI-150899. © IOS Press and the authors. All rights reserved. Creative Commons BY-NC-SA.
Preliminary Statistical Analysis of Documentation of Killings in the Syrian Arab Republic.
Megan Price, Jeff Klingner, and Patrick Ball (2013). The Benetech Human Rights Program, commissioned by the United Nations Office of the High Commissioner for Human Rights (OHCHR). January 2, 2013. © 2013 HRDAG. Creative Commons BY-NC-SA.
Reflections: The G in HRDAG is the Real Fuel
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.”
Making the Case: The Role of Statistics in Human Rights Reporting.
Patrick Ball. “Making the Case: The Role of Statistics in Human Rights Reporting.” Statistical Journal of the United Nations Economic Commission for Europe. 18(2-3):163-174. 2001.