Megan Price: "How Machine Learning Helps Count Casualties in Syria"
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Speaker: Megan Price, Executive Director of the Human Rights Data Analysis Group
Title: How Machine Learning Helps Count Casualties in Syria
Abstract:
A fundamental question in any conflict is 'how many people have been killed?' This seemingly straight-forward question is surprisingly difficult to answer even during times of peace and stability. It becomes virtually impossible to answer during the fog of war. In the case of Syria, numerous individuals and organizations are committed to the dangerous work of documenting victims of the armed conflict. The result of these efforts are multiple lists of tens or hundreds of thousands of named victims. Machine learning techniques can help to make sense of these lists, identifying multiple records that refer to the same victim, even when those records may contain incomplete or contradictory information. Combining records from these multiple sources into a single list is only the first step - this answers the question 'how many victims have been identified by at least one source?' To answer the fundamental question about how many people have been killed, we must use statistical models to estimate what we do not know - to estimate the number of victims who have not yet been reported to any source. Only then can we draw accurate conclusions about patterns of violence and make evidence-based policy recommendations.
Bio:
Megan is a Research Fellow at the Carnegie Mellon University Center for Human Rights Science, and she is the Human Rights Editor for the Statistical Journal of the International Association for Official Statistics (IAOS). She earned her doctorate in biostatistics and a Certificate in Human Rights from the Rollins School of Public Health at Emory University. She also holds a master of science degree and bachelor of science degree in Statistics from Case Western Reserve University.
From 2013 through 2015, Megan was the Director of Research at HRDAG; on December 1, 2015, she became Executive Director.