Prior studies on the effectiveness of professional jury consultants in predicting juror proclivities have yielded mixed results, and few have rigorously evaluated consultant performance against chance under controlled conditions. This study addresses that gap by empirically assessing whether jury consultants can reliably predict juror predispositions beyond chance levels and whether supervised machine-learning (ML) models can outperform consultant predictions. Using data from N mock jurors who completed pre-trial attitudinal questionnaires and rendered verdicts in a standardized wrongful-termination case, we compared predictions made by professional jury consultants with those generated by Random Forest (RF) and k-Nearest Neighbors (KNN) classifiers. Model and consultant predictions were evaluated on a held-out test set using paired statistical tests and nonparametric bootstrap procedures. We find that supervised ML models significantly outperform professional jury consultants under identical informational constraints, while offering greater transparency, replicability, and auditability. These results provide an empirical benchmark for evaluating human judgment in jury selection and inform ongoing debates about the role of data-driven decision support in legal contexts. To support reproducibility and auditability, all code and data will be made publicly available upon publication.

翻译：先前关于专业陪审团顾问预测陪审员倾向有效性的研究结果不一，且鲜有研究在受控条件下严格评估顾问表现与随机水平的差异。本研究通过实证评估填补了这一空白：检验陪审团顾问能否可靠地预测超出随机水平的陪审员倾向，以及监督式机器学习模型能否超越顾问的预测能力。基于N名模拟陪审员在标准化不当解雇案件中完成的审前态度问卷及裁决数据，我们将专业陪审团顾问的预测与随机森林和K近邻分类器的预测结果进行比较。通过配对统计检验和非参数自助法程序，在预留测试集上评估了模型与顾问的预测表现。研究发现，在相同信息约束条件下，监督式机器学习模型显著优于专业陪审团顾问，同时提供更高的透明度、可复现性和可审计性。这些结果为评估陪审团遴选过程中的人类判断提供了实证基准，并为法律领域中数据驱动决策支持作用的持续讨论提供了依据。为支持可复现性与可审计性，所有代码与数据将在发表后公开。