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Matching the Libro Amarillo to Historical Human Rights Datasets in El Salvador
Patrick Ball (2014). A memo accompanying the release of The Yellow Book. August 20, 2014. © 2014 HRDAG. Creative Commons BY-NC-SA.[pdf español]
Working Where Statistics and Human Rights Meet
Robin Mejia and Megan Price (2018). Working Where Statistics and Human Rights Meet. Chance (special issue). February 2018. © 2018 CHANCE.
Evaluation of the Kosovo Memory Book at Pristina
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.
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.
How a Data Tool Tracks Police Misconduct and Wandering Officers
Measuring the Mortality Consequences of Armed Conflict in Amritsar, India: A New Approach to the Indirect Sampling of Conflict-Related Mortality
Romesh Silva and Jeff Klingner. “Measuring the Mortality Consequences of Armed Conflict in Amritsar, India: A New Approach to the Indirect Sampling of Conflict-Related Mortality.” Poster presented at the Population Association of America 2011 Annual Meeting. © 2011 Benetech. Creative Commons BY-NC-SA.
Una Mirada al Archivo Histórico de la Policia Nacional a Partir de un Estudio Cuantitativo
Carolina López, Beatriz Vejarano, and Megan Price. 2016. Human Rights Data Analysis Group. © 2016 HRDAG.Creative Commons BY-NC-SA.
Letter from the Executive Director
How Data Extraction Illuminates Racial Disparities in Boston SWAT Raids
Megan Price Elected Board Member of Tor Project
Learning a Modular, Auditable and Reproducible Workflow
Primer to Inform Discussions about Bail Reform
Rise of the racist robots – how AI is learning all our worst impulses
“If you’re not careful, you risk automating the exact same biases these programs are supposed to eliminate,” says Kristian Lum, the lead statistician at the San Francisco-based, non-profit Human Rights Data Analysis Group (HRDAG). Last year, Lum and a co-author showed that PredPol, a program for police departments that predicts hotspots where future crime might occur, could potentially get stuck in a feedback loop of over-policing majority black and brown neighbourhoods. The program was “learning” from previous crime reports. For Samuel Sinyangwe, a justice activist and policy researcher, this kind of approach is “especially nefarious” because police can say: “We’re not being biased, we’re just doing what the math tells us.” And the public perception might be that the algorithms are impartial.
Foundation of Human Rights Statistics in Sierra Leone
Richard Conibere (2004). Foundation of Human Rights Statistics in Sierra Leone (abstr.), Joint Statistical Meetings. Toronto, Canada.