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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.
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 Predictive Analytics Comes to Academic and Nonprofit Institutions to Fuel Innovation
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.
The True Dangers of AI are Closer Than We Think
William Isaac is quoted.
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.”
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.”
Overbooking’s Impact on Pre-Trial Risk Assessment Tools
Nonprofits Are Taking a Wide-Eyed Look at What Data Could Do
Coming soon: HRDAG 2019 Year-End Review
How We Choose Projects
Lies, Damned Lies and Official Statistics
Primer to Inform Discussions about Bail Reform
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.
…
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.
Without Encryption, My Work Wouldn’t Be Possible
An Award for Anita Gohdes
Estimating the human toll in Syria
Megan Price (2017). Estimating the human toll in Syria. Nature. 8 February 2017. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. Nature Human Behaviour. ISSN 2397-3374.
Cuentas y mediciones de la criminalidad y de la violencia
Exploración y análisis de los datas para comprender la realidad. Patrick Ball y Michael Reed Hurtado. 2015. Forensis 16, no. 1 (July): 529-545. © 2015 Instituto Nacional de Medicina Legal y Ciencias Forenses (República de Colombia).