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R programming language demands the right use case
Megan Price, director of research, is quoted in this story about the R programming language. “Serious data analysis is not something you’re going to do using a mouse and drop-down boxes,” said HRDAG’s director of research Megan Price. “It’s the kind of thing you’re going to do getting close to the data, getting close to the code and writing some of it yourself.”
Africa
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 ‘Dirty War Index’ and the Real World of Armed Conflict.
Amelia Hoover, Romesh Silva, Tamy Guberek, and Daniel Guzmán. “The ‘Dirty War Index’ and the Real World of Armed Conflict.” May 23, 2009. © 2009 HRDAG. Creative Commons BY-NC-SA.
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.
Truth and Myth in Sierra Leone: An Empirical Analysis of the Conflict, 1991–2000
Tamy Guberek, Daniel Guzmán, Romesh Silva, Kristen Cibelli, Jana Asher, Scott Weikart, Patrick Ball, and Wendy Grossman. “Truth and Myth in Sierra Leone: An Empirical Analysis of the Conflict, 1991–2000″ (pdf). A report by the Benetech Human Rights Data Analysis Group and the American Bar Association. March 28, 2006.
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.
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.
Studying Millions of Rescued Documents: Sampling Plan at the Guatemalan National Police Archive (GNPA).
Daniel R. Guzmán, Tamy Guberek, Gary M. Shapiro, Paul Zador (2009). “Studying Millions of Rescued Documents: Sampling Plan at the Guatemalan National Police Archive (GNPA).” In JSM Proceedings, Survey Research Methods Section. Alexandria, VA: American Statistical Association.
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.
State Coordinated Violence in Chad under Hissene Habre: A Statistical Analysis of Reported Prison Mortality in Chad’s DDS Prisons and Command Responsibility of Hissene Habre, 1982-1990.
Romesh Silva, Jeff Klingner, and Scott Weikart. “State Coordinated Violence in Chad under Hissene Habre: A Statistical Analysis of Reported Prison Mortality in Chad’s DDS Prisons and Command Responsibility of Hissene Habre, 1982-1990.” A Report by Benetech’s Human Rights Data Analysis Group to Human Rights Watch and the Chadian Association of Victims of Political Repression and Crimes. 29 January 2010. (Available in French) © 2010 Benetech. 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.
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 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.
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.