Current machine learning models produce outstanding results in many areas but, at the same time, suffer from shortcut learning and spurious correlations. To address such flaws, the explanatory interactive machine learning (XIL) framework has been proposed to revise a model by employing user feedback on a model's explanation. This work sheds light on the explanations used within this framework. In particular, we investigate simultaneous model revision through multiple explanation methods. To this end, we identified that \textit{one explanation does not fit XIL} and propose considering multiple ones when revising models via XIL.

翻译：当前的机器学习模型在许多领域取得了显著成果，但同时存在捷径学习和虚假相关性的问题。为解决这些缺陷，研究人员提出了解释性交互式机器学习（XIL）框架，通过采纳用户对模型解释的反馈来修正模型。本研究揭示了该框架中使用的解释的实质，特别探究了通过多种解释方法进行同步模型修正的可能性。为此，我们发现了"一种解释无法适用于XIL"的现象，并提出在通过XIL修正模型时应考虑多种解释方法。