On the Fairness of Death-Penalty Jurors: A Comparison of Bayesian Models with Different Levels of Hierarchy and Various Missing-Data Mechanisms
死刑陪审员的公正性：不同层级与缺失机制下的贝叶斯模型比较AI 解读 ↓
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死刑陪审员的公正性：不同层级与缺失机制下的贝叶斯模型比较
- 研究问题
- 反对死刑而会被排除的陪审员，与不会被排除者，其有罪投票概率是否不同，层级结构与未回答情况如何影响判断？
- 方法与思路
- 作者使用非死刑案件结束后的陪审员调查，建立含陪审团效应的贝叶斯层级投票模型，用 Gibbs 抽样并比较缺失机制。
- 材料陈述的贡献
- 摘要称，后验对数优势比较提示非死刑案件中两类陪审员可能存在决策差异，并讨论不同层级模型的先验可比性。
- 阅读关注点
- 应核对陪审团效应、不同模型间先验匹配、缺失机制敏感性，以及从非死刑案件推及死刑案件的证据是否充分。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；数据来自非死刑案件，对死刑案件的差异仅是作者推测，也不能据此确认因果效应。
原文摘要
Jurors who are opposed to the death penalty are excluded from serving on trials in which the defendant may be sentenced to death. The exclusion of such jurors from death-penalty trials raises some concern about the fairness of these juries in deciding the guilt or innocence of a defendant. This article uses data from a survey of behaviors and beliefs of jurors conducted at the conclusion of non–capital offense trials to study this issue. A Bayesian hierarchical model is proposed for the probabilities of voting guilty or not guilty on a particular trial given various death-penalty beliefs. The Gibbs sampler is used to fit the model. The posterior distributions of the log odds of voting guilty for jurors who would be excluded from death-penalty juries versus those who would not be excluded suggest that there may be differences between the decisions of jurors on non–capital offense trials. We believe that they are likely to differ on death-penalty trials as well. The results from fitting the hierarchical model, which includes a jury effect, are compared with a previous analysis by Kadane that took the jurors' responses to be independent. At issue is the comparability of the priors under models with different hierarchical structures. Another issue in the analysis is that some jurors did not respond to the survey concerning their death-penalty beliefs. Because the views of nonrespondents may differ from those of respondents, our analysis includes an investigation of how various assumptions about the missing-data mechanism affect the conclusions.