On the Fairness of Death-Penalty Jurors: A Comparison of Bayesian Models with Different Levels of Hierarchy and Various Missing-Data Mechanisms
研究排除反对死刑的陪审员是否影响审判公平，通过贝叶斯分层模型分析非死刑案件陪审员投票行为，发现被排除者与未被排除者的投票倾向可能存在差异。
Abstract 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.