680 results for search: %E3%80%88%ED%95%98%EC%95%882%EB%8F%99%EB%8F%99%EC%95%84%EB%A6%AC%E3%80%89%20WWW-MEDA-PW%20%20%EA%B9%80%EC%A0%9C%EB%8C%81%EC%86%8C%EA%B0%9C%ED%8C%85%EC%96%B4%ED%94%8C%20%EA%B9%80%EC%A0%9C%EB%8C%81%EC%86%8C%EC%85%9C%D1%86%EA%B9%80%EC%A0%9C%EB%8C%81%EC%86%94%EB%A1%9C%D1%8B%EA%B9%80%EC%A0%9C%EB%8C%81%EC%88%9C%EC%9C%84%E3%8B%B2%E3%82%87%E8%92%80secretory/feed/content/colombia/Co-union-violence-paper-response.pdf
HRDAG Adds Three New Board Members
Celebrating our First Anniversary and Welcoming Our Newest Board Member
Clustering and Solving the Right Problem
Recognising Uncertainty in Statistics
In Responsible Data Reflection Story #7—from the Responsible Data Forum—work by HRDAG affiliates Anita Gohdes and Brian Root is cited extensively to make the point about how quantitative data are the result of numerous subjective human decisions. An excerpt: “The Human Rights Data Analysis Group are pioneering the way in collecting and analysing figures of killings in conflict in a responsible way, using multiple systems estimation.”
Collecting Sensitive Human Rights Data in the Field: A Case Study from Amritsar, India.
Romesh Silva and Jasmine Marwaha. “Collecting Sensitive Human Rights Data in the Field: A Case Study from Amritsar, India.” In JSM Proceedings, Social Statistics Section. Alexandria, VA. © 2011 American Statistical Association. All rights reserved.
Making the Case. Investigating Large Scale Human Rights Violations Using Information Systems and Data Analysis
Patrick Ball, Herbert F. Spirer, and Louise Spirer, eds. Making the Case. Investigating Large Scale Human Rights Violations Using Information Systems and Data Analysis . © 2000 American Association for the Advancement of Science. All rights reserved. Reprinted with permission. [full text] [intro] [chapters 1 2 3 4 5 67 8 9 10 11 12]
Different Convenience Samples, Different Stories: The Case of Sierra Leone.
Anita Gohdes. “Different Convenience Samples, Different Stories: The Case of Sierra Leone.” Benetech. 2010. © 2010 Benetech. Creative Commons BY-NC-SA.
On ensuring a higher level of data quality when documenting human rights violations to support research into the origins and cause of human rights violations
Romesh Silva. “On ensuring a higher level of data quality when documenting human rights violations to support research into the origins and cause of human rights violations.” ASA Proceedings of the Joint Statistical Meetings, the International Biometric Society (ENAR and WNAR), the Institute of Mathematical Statistics, and the Statistical Society of Canada. August, 2002.
Preliminary Statistical Analysis of Documentation of Killings in the Syrian Arab Republic.
Megan Price, Jeff Klingner, and Patrick Ball (2013). The Benetech Human Rights Program, commissioned by the United Nations Office of the High Commissioner for Human Rights (OHCHR). January 2, 2013. © 2013 HRDAG. Creative Commons BY-NC-SA.
Using Machine Learning to Help Human Rights Investigators Sift Massive Datasets
Changes at HRDAG
About HRDAG
Where Stats and Rights Thrive Together
War and Illness Could Kill 85,000 Gazans in 6 Months
HRDAG director of research Patrick Ball is quoted in this New York Times article about a paper that models death tolls in Gaza.
Momentous Verdict against Hissène Habré
Human Rights Violations: How Do We Begin Counting the Dead?
Big Data Predictive Analytics Comes to Academic and Nonprofit Institutions to Fuel Innovation
Celebrating Women in Statistics
In her work on statistical issues in criminal justice, Lum has studied uses of predictive policing—machine learning models to predict who will commit future crime or where it will occur. In her work, she has demonstrated that if the training data encodes historical patterns of racially disparate enforcement, predictions from software trained with this data will reinforce and—in some cases—amplify this bias. She also currently works on statistical issues related to criminal “risk assessment” models used to inform judicial decision-making. As part of this thread, she has developed statistical methods for removing sensitive information from training data, guaranteeing “fair” predictions with respect to sensitive variables such as race and gender. Lum is active in the fairness, accountability, and transparency (FAT) community and serves on the steering committee of FAT, a conference that brings together researchers and practitioners interested in fairness, accountability, and transparency in socio-technical systems.
Palantir Has Secretly Been Using New Orleans to Test Its Predictive Policing Technology
One of the researchers, a Michigan State PhD candidate named William Isaac, had not previously heard of New Orleans’ partnership with Palantir, but he recognized the data-mapping model at the heart of the program. “I think the data they’re using, there are serious questions about its predictive power. We’ve seen very little about its ability to forecast violent crime,” Isaac said.