Recent work has shown that models trained to the same objective, and which
achieve similar measures of accuracy on consistent test data, may nonetheless
behave very differently on individual predictions. This inconsistency is
undesirable in high-stakes contexts, such as medical diagnosis and finance. We
show that this inconsistent behavior extends beyond predictions to feature
attributions, which may likewise have negative implications for the
intelligibility of a model, and one's ability to find recourse for subjects. We
then introduce selective ensembles to mitigate such inconsistencies by applying
hypothesis testing to the predictions of a set of models trained using
randomly-selected starting conditions; importantly, selective ensembles can
abstain in cases where a consistent outcome cannot be achieved up to a
specified confidence level. We prove that that prediction disagreement between
selective ensembles is bounded, and empirically demonstrate that selective
ensembles achieve consistent predictions and feature attributions while
maintaining low abstention rates. On several benchmark datasets, selective
ensembles reach zero inconsistently predicted points, with abstention rates as
low 1.5%.

通过引入选择性集合来减轻模型的不一致性，使用随机选定的起始条件训练一组模型的预测上执行假设检验；在这些情况下，选择性集合可以放弃不一致预测结果并在指定的置信水平下达成一致结果，多个基准数据集上展示出零一致性预测点和低至 1.5％的放弃率。