698 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/rss2/chad-photo-essay/privacy
How do epidemiologists know how many people will get Covid-19?
Patrick Ball (2020). How do epidemiologists know how many people will get Covid-19? Significance. 09 April 2020. © 2020 The Royal Statistical Society.
How many people are going to die from COVID-19?
Patrick Ball, Kristian Lum, Tarak Shah and Megan Price (2020). How many people are going to die from COVID-19? Granta. 14 March 2020. © Granta Publications 2020.
Counting the Dead in Sri Lanka
How Machine Learning Makes Visible Gender-Based Violence by Police
Sierra Leone
An Award for Anita Gohdes
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]
Predictive policing violates more than it protects
William Isaac and Kristian Lum. Predictive policing violates more than it protects. USA Today. December 2, 2016. © USA Today.
Data Science Symposium at Vanderbilt
HRDAG’s Year in Review: 2022
Contact Us
Counting Civilian Casualties: An Introduction to Recording and Estimating Nonmilitary Deaths in Conflict
ed. by Taylor B. Seybolt, Jay D. Aronson, and Baruch Fischhoff. Oxford University Press. © 2013 Oxford University Press. All rights reserved.
The following four chapters are included:
— Todd Landman and Anita Gohdes (2013). “A Matter of Convenience: Challenges of Non-Random Data in Analyzing Human Rights Violations in Peru and Sierra Leone.”
— Jeff Klingner and Romesh Silva (2013). “Combining Found Data and Surveys to Measure Conflict Mortality.”
— Daniel Manrique-Vallier, Megan E. Price, and Anita Gohdes (2013). “Multiple-Systems Estimation Techniques for Estimating Casualties in Armed Conflict.”
— Jule Krüger, Patrick Ball, Megan Price, and Amelia Hoover Green (2013). “It Doesn’t Add Up: Methodological and Policy Implications of Conflicting Casualty Data.”
Donate
Reflections: Growing and Learning in Guatemala
Karl E. Peace Award Recognizes Work of Patrick Ball
The American Statistical Association’s 2018 Karl E. Peace Award for Outstanding Statistical Contributions for the Betterment of Society recently recognized the work of leading human rights mathematician Patrick Ball of the Human Rights Data Analysis Group (HRDAG). The award is presented annually to statisticians whose exemplary statistical research is matched by the impact their work has had on the lives of people.
Established by the family of Karl E. Peace in honor of his work for the good of society, the award—announced at the Joint Statistical Meetings—is bestowed upon distinguished individual(s) who have made substantial contributions to the statistical profession, contributions that have led in direct ways to improving the human condition. Recipients will have demonstrated through their accomplishments their commitment to service for the greater good.”
This year, Ball became the 10th recipient of the award. Read more …
The Allegheny Family Screening Tool’s Overestimation of Utility and Risk
Anjana Samant, Noam Shemtov, Kath Xu, Sophie Beiers, Marissa Gerchick, Ana Gutierrez, Aaron Horowitz, Tobi Jegede, Tarak Shah (2023). The Allegheny Family Screening Tool’s Overestimation of Utility and Risk. Logic(s). 13 December, 2023. Issue 20.
Featured Video
Quantifying Injustice
“In 2016, two researchers, the statistician Kristian Lum and the political scientist William Isaac, set out to measure the bias in predictive policing algorithms. They chose as their example a program called PredPol. … Lum and Isaac faced a conundrum: if official data on crimes is biased, how can you test a crime prediction model? To solve this technique, they turned to a technique used in statistics and machine learning called the synthetic population.”