Using Statistics to Assess Lethal Violence in Civil and Inter-State War

Abstract

What role can statistics play in assessing the patterns of lethal violence in conflict? This article highlights the evolution of statistical applications in assessing lethal violence, from the presentation of data in the Nuremberg trials to current questions around machine learning and training data. We present examples from work conducted by our organization, the Human Rights Data Analysis Group, and others, primarily researching killings in the context of civil wars and international conflict. The primary challenge we encounter in this work is the question of whether observed patterns of violence represent the true underlying pattern or are a reflection of reports of violence, which are subject to many sources of bias. This is where we find the foundations of twentieth-century statistics to be most important: Is this sample representative? What methods are best suited to reduce the bias in nonprobability samples? These questions lead us to the approaches presented here: multiple systems estimation, surveys, complete data, and the question of bias within training data for machine learning models. We close with memories of Steve Fienberg's influence on these questions and on us personally. “It's all inference,” he told us, and that insight informs our concerns about bias in data used to create historical memory and advance justice in the wake of mass violence.