Deep neural networks have shown excellent performances in many real-world applications. Unfortunately, they may show "Clever Hans"-like behavior -- making use of confounding factors within datasets -- to achieve high performance. In this work, we introduce the novel learning setting of "explanatory interactive learning" (XIL) and illustrate its benefits on a plant phenotyping research task. XIL adds the scientist into the training loop such that she interactively revises the original model via providing feedback on its explanations. Our experimental results demonstrate that XIL can help avoiding Clever Hans moments in machine learning and encourages (or discourages, if appropriate) trust into the underlying model.

翻译：深度神经网络已在许多实际应用中展现出卓越性能。然而，它们可能表现出"聪慧汉斯"式行为——利用数据集中的混淆因子——来获得高性能。本研究提出了一种新颖的学习范式"解释性交互学习"，并展示了其在植物表型研究任务中的优势。XIL将科学家纳入训练循环，使其能够通过提供对模型解释的反馈来交互式修正原始模型。实验结果表明，XIL有助于避免机器学习中的"聪慧汉斯"时刻，并能促进（或在适当时抑制）对底层模型的信任。