679 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/copyright
About HRDAG
Changes at HRDAG
Using Machine Learning to Help Human Rights Investigators Sift Massive Datasets
Where Stats and Rights Thrive Together
Momentous Verdict against Hissène Habré
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.
First Things First: Assessing Data Quality Before Model Quality.
Anita Gohdes and Megan Price (2013). Journal of Conflict Resolution, Volume 57 Issue 6 December 2013. © 2013 Journal of Conflict Resolution. All rights reserved. Reprinted with permission of SAGE. [online abstract]DOI: 10.1177/0022002712459708.
Big Data, Selection Bias, and the Statistical Patterns of Mortality in Conflict
Megan Price and Patrick Ball (2014). SAIS Review of International Affairs © 2014 The Johns Hopkins University Press. This article first appeared in SAIS Review, Volume 34, Issue 1, Winter-Spring 2014, pages 9-20. All rights reserved.
Evaluation of the Database of the Kosovo Memory Book
Jule Krüger and Patrick Ball (2014). An analysis accompanying the release of the Kosovo Memory Book. December 10, 2014. © 2014 HRDAG. Creative Commons BY-NC-SA.
Big Data Predictive Analytics Comes to Academic and Nonprofit Institutions to Fuel Innovation
Welcoming a New Board Member
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.
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.
Middle East
Liberian Truth and Reconciliation Commission Data
Beautiful game, ugly truth?
Megan Price (2022). Beautiful game, ugly truth? Significance, 19: 18-21. December 2022. © The Royal Statistical Society. https://doi.org/10.1111/1740-9713.01702