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Why top funders back this small human rights organization with a global reach
Eric Sears, a director at the MacArthur Foundation who leads the grantmaker’s Technology in the Public Interest program, worked at Human Rights First and Amnesty International before joining MacArthur, and has been following HRDAG’s work for years. … One of HRDAG’s strengths is the long relationships it maintains with partners around the globe. “HRDAG is notable in that it really develops deep relationships and partnerships and trust with organizations and actors in different parts of the world,” Sears said. “I think they’re unique in the sense that they don’t parachute into a situation and do a project and leave. They tend to stick with organizations and with issues over the long term, and continually help build cases around evidence and documentation to ensure that when the day comes, when accountability is possible, the facts and the evidence are there.”
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.
Using Quantitative Data to Assess Conflict-Related Sexual Violence in Colombia: Challenges and Opportunities.
Françoise Roth, Tamy Guberek, and Amelia Hoover Green. “Using Quantitative Data to Assess Conflict-Related Sexual Violence in Colombia: Challenges and Opportunities.” A report by the Benetech Human Rights Program and Corporación Punto de Vista. 22 March 2011. (Spanish.) © 2011 Benetech. Creative Commons BY-NC-SA.
Measuring Elusive Populations with Bayesian Model Averaging for Multiple Systems Estimation: A Case Study on Lethal Violations in Casanare, 1998-2007
Kristian Lum, Megan Price, Tamy Guberek, and Patrick Ball. “Measuring Elusive Populations with Bayesian Model Averaging for Multiple Systems Estimation: A Case Study on Lethal Violations in Casanare, 1998-2007,” Statistics, Politics, and Policy. 1(1) 2010. All rights reserved.
To Count the Uncounted: An Estimation of Lethal Violence in Casanare,
Tamy Guberek, Daniel Guzmán, Megan Price, Kristian Lum and Patrick Ball, “To Count the Uncounted: An Estimation of Lethal Violence in Casanare,” A Report by the Benetech Human Rights Program. 10 February 2010. (Available in Spanish) © 2010 Benetech. Creative Commons BY-NC-SA.
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.
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.
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?”
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 ‘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.
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.
Assessing Claims of Declining Lethal Violence in Colombia
Patrick Ball, Tamy Guberek, Daniel Guzmán, Amelia Hoover, and Meghan Lynch (2007). “Assessing Claims of Declining Lethal Violence in Colombia.” Benetech. Also available in Spanish – “Para Evaluar Afirmaciones Sobre la Reducción de la Violencia Letal en Colombia.”
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)
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.