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Preliminary Statistical Analysis of AVCRP & DDS Documents – A report to Human Rights Watch about Chad under the government of Hissène Habré
Miguel Cruz, Kristen Cibelli, and Jana Dudukovic. “Preliminary Statistical Analysis of AVCRP & DDS Documents – A report to Human Rights Watch about Chad under the government of Hissène Habré”. Benetech. November 4, 2003.
Sierra Leone Statistical Appendix
Richard Conibere, Jana Asher, Kristen Cibella, Jana Dudukovic, Rafe Kaplan, and Patrick Ball. Sierra Leone Statistical Appendix, A Report by the Benetech Human Rights Data Analysis Group and the American Bar Association Central European and Eurasian Law Initiative to the Truth and Reconciliation Commission. October 5, 2004.
Uncovering Police Violence in Chicago: A collaboration between HRDAG and Invisible Institute
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)]
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)
Seeking the Truth with Documentation
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.
Death rate in Habre jails higher than for Japanese POWs, trial told
Patrick Ball of the California-based Human Rights Data Analysis Group said he had calculated the mortality rate of political prisoners from 1985 to 1988 using reports completed by Habre’s feared secret police.
Data and Social Good: Using Data Science to Improve Lives, Fight Injustice, and Support Democracy
In this free, downloadable report, Mike Barlow of O’Reilly Media cites several examples of how data and the work of data scientists have made a measurable impact on organizations such as DataKind, a group that connects socially minded data scientists with organizations working to address critical humanitarian issues. HRDAG—and executive director Megan Price—is one of the first organizations whose work is mentioned.
Using Data to Reveal Human Rights Abuses
Profile touching on HRDAG’s work on the trial and conviction of Hissène Habré, its US Policing Project, data integrity, data archaeology and more.
Data-driven development needs both social and computer scientists
Excerpt:
Data scientists are programmers who ignore probability but like pretty graphs, said Patrick Ball, a statistician and human rights advocate who cofounded the Human Rights Data Analysis Group.
“Data is broken,” Ball said. “Anyone who thinks they’re going to use big data to solve a problem is already on the path to fantasy land.”
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?”
Counting the Unknown Victims of Political Violence: The Work of the Human Rights Data Analysis Group
Ann Harrison (2012). Counting the Unknown Victims of Political Violence: The Work of the Human Rights Data Analysis Group, in Human Rights and Information Communications Technologies: Trends and Consequences of Use. © 2012 IGI Global. All rights reserved.
Limitations of mitigating judicial bias with machine learning
Kristian Lum (2017). Limitations of mitigating judicial bias with machine learning. Nature. 26 June 2017. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. Nature Human Behavior. DOI 10.1038/s41562-017-0141.
Data on Kosovo Killings
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.
What HBR Gets Wrong About Algorithms and Bias
“Kristian Lum… organized a workshop together with Elizabeth Bender, a staff attorney for the NY Legal Aid Society and former public defender, and Terrence Wilkerson, an innocent man who had been arrested and could not afford bail. Together, they shared first hand experience about the obstacles and inefficiencies that occur in the legal system, providing valuable context to the debate around COMPAS.”
The Data Scientist Helping to Create Ethical Robots
Kristian Lum is focusing on artificial intelligence and the controversial use of predictive policing and sentencing programs.
What’s the relationship between statistics and AI and machine learning?
AI seems to be a sort of catchall for predictive modeling and computer modeling. There was this great tweet that said something like, “It’s AI when you’re trying to raise money, ML when you’re trying to hire developers, and statistics when you’re actually doing it.” I thought that was pretty accurate.
A better statistical estimation of known Syrian war victims
Researchers from Rice University and Duke University are using the tools of statistics and data science in collaboration with Human Rights Data Analysis Group (HRDAG) to accurately and efficiently estimate the number of identified victims killed in the Syrian civil war.
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Using records from four databases of people killed in the Syrian war, Chen, Duke statistician and machine learning expert Rebecca Steorts and Rice computer scientist Anshumali Shrivastava estimated there were 191,874 unique individuals documented from March 2011 to April 2014. That’s very close to the estimate of 191,369 compiled in 2014 by HRDAG, a nonprofit that helps build scientifically defensible, evidence-based arguments of human rights violations.