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

机译：使用统计数据评估民事和州间战争中的致命暴力

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摘要

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. Theprimary 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.
机译：在评估冲突中的致命暴力模式方面发挥了什么作用统计？本文突出了评估致命暴力的统计应用的演变;从纽伦堡中的数据呈现到机器学习和培训数据周围的当前问题。我们提出了我们组织，人权数据分析小组等工作的例子，主要在内战和国际冲突中研究杀戮。我们在这项工作中遇到的挑战是观察到的暴力模式是否代表了真正的潜在模式，或者反映了暴力报告，这符合许多偏见来源。这是我们发现二十世纪统计数据的基础是最重要的：这是这个示例代表吗？最适合在不可驾失性样品中减少偏差的方法是什么方法？这些问题导致我们到此处提供的方法：多个系统估算，调查，完整数据以及机器学习模型的培训数据内的偏差问题。我们与史蒂夫费恩伯格对这些问题的影响和我们个人的影响密切相关。 “这都是所有推论，”他告诉我们，洞察力告诉我们对用于创造历史记忆的数据的偏见，并在大规模暴力之后推进司法。

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