This work addresses the challenge of providing consistent explanations for predictive models in the presence of model indeterminacy, which arises due to the existence of multiple (nearly) equally well-performing models for a given dataset and task. Despite their similar performance, such models often exhibit inconsistent or even contradictory explanations for their predictions, posing challenges to end users who rely on these models to make critical decisions. Recognizing this issue, we introduce ensemble methods as an approach to enhance the consistency of the explanations provided in these scenarios. Leveraging insights from recent work on neural network loss landscapes and mode connectivity, we devise ensemble strategies to efficiently explore the $\textit{underspecification set}$ -- the set of models with performance variations resulting solely from changes in the random seed during training. Experiments on five benchmark financial datasets reveal that ensembling can yield significant improvements when it comes to explanation similarity, and demonstrate the potential of existing ensemble methods to explore the underspecification set efficiently. Our findings highlight the importance of considering model indeterminacy when interpreting explanations and showcase the effectiveness of ensembles in enhancing the reliability of explanations in machine learning.

翻译：本研究旨在解决预测模型在存在模型不确定性时提供一致解释的挑战。模型不确定性源于针对特定数据集和任务存在多个（近乎）同等性能的模型。尽管这些模型表现相似，但它们对其预测结果往往展现出不一致甚至相互矛盾的解释，这给依赖这些模型做出关键决策的终端用户带来了挑战。针对该问题，我们提出将集成方法作为增强此类场景下解释一致性的途径。通过借鉴神经网络损失景观和模式连通性的最新研究成果，我们设计了集成策略以高效探索欠规范集——即仅因训练过程中随机种子变化而导致性能波动的模型集合。在五个金融基准数据集上的实验表明，集成方法在提升解释相似性方面能带来显著改进，并验证了现有集成方法高效探索欠规范集的潜力。我们的发现强调了在解释模型时考虑模型不确定性的重要性，并展示了集成方法在增强机器学习解释可靠性方面的有效性。