We present a method of explainable artificial intelligence (XAI), "What I Know (WIK)", to provide additional information to verify the reliability of a deep learning model by showing an example of an instance in a training dataset that is similar to the input data to be inferred and demonstrate it in a remote sensing image classification task. One of the expected roles of XAI methods is verifying whether inferences of a trained machine learning model are valid for an application, and it is an important factor that what datasets are used for training the model as well as the model architecture. Our data-centric approach can help determine whether the training dataset is sufficient for each inference by checking the selected example data. If the selected example looks similar to the input data, we can confirm that the model was not trained on a dataset with a feature distribution far from the feature of the input data. With this method, the criteria for selecting an example are not merely data similarity with the input data but also data similarity in the context of the model task. Using a remote sensing image dataset from the Sentinel-2 satellite, the concept was successfully demonstrated with reasonably selected examples. This method can be applied to various machine-learning tasks, including classification and regression.

翻译：我们提出了一种可解释人工智能（XAI）方法——“我所知道的（WIK）”，通过展示与待推断输入数据相似且来自训练数据集的实例样本，为验证深度学习模型的可靠性提供额外信息，并在遥感图像分类任务中进行了演示。XAI方法的预期功能之一是验证已训练机器学习模型的推断在应用中是否有效，而训练模型所用的数据集以及模型架构均为重要因素。这种以数据为中心的方法可通过检查所选样例数据，帮助判断训练数据集是否足以支撑每次推断。若所选样例与输入数据相似，则可确认模型并未基于与输入数据特征分布差异过大的数据集进行训练。该方法筛选样例的准则不仅基于与输入数据的数据相似度，还考虑了模型任务上下文中的数据相似性。通过使用哨兵二号（Sentinel-2）卫星的遥感图像数据集，我们成功展示了该方法的理念，并生成了合理的样例。该方法可应用于包括分类与回归在内的各类机器学习任务。