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中都产生了卓越的效果，但同时也存在捷径学习和伪相关性的问题。为了解决这些缺陷，提出了解释性交互式机器学习（XIL）框架，通过采用用户对模型解释的反馈来修正模型。本文阐明了在该框架内使用的解释方法。我们特别研究了多重解释方法在联合学习中的应用。因此，我们确定了“XIL框架并非万能药”的观点，并建议在通过XIL修正模型时考虑多重解释。