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A Model to Estimate SARS-CoV-2-Positive Americans
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.”
Can We Harness AI To Fulfill The Promise Of Universal Human Rights?
The Human Rights Data Analysis Group employs AI to analyze data from conflict zones, identifying patterns of human rights abuses that might be overlooked. This assists international organizations in holding perpetrators accountable.
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.
Megan Price: Life-Long ‘Math Nerd’ Finds Career in Social Justice
“I was always a math nerd. My mother has a polaroid of me in the fourth grade with my science fair project … . It was the history of mathematics. In college, I was a math major for a year and then switched to statistics.
I always wanted to work in social justice. I was raised by hippies, went to protests when I was young. I always felt I had an obligation to make the world a little bit better.”
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.
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 TRC Data and Statistical Appendix
Political Killings in Kosovo, March–June, 1999
Political Killings in Kosovo, March–June, 1999. American Association for the Advancement of Science, Science and Human Rights Program. © 2000 American Bar Association Central and East European Law Initiative.
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.
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.
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.
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.
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.
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.