这篇论文给了你可解释AI的新思路：用偏依赖约束让模型学得更准，还能确保解释符合常识，尤其适合回归问题。
论文提出一种通过偏依赖约束引导神经网络训练的新方法，使模型对特定特征的响应符合先验知识。在多个回归任务（包括动态系统预测）上的实验表明，该方法训练的模型比无约束模型性能更好且数据效率更高。从约束模型得到的解释与实际用户知识一致，而无约束模型则不然。
Steering Neural Network Training through Interpretable Constraints Based on Partial Dependence
Over the last few years, there has been an increased interest in making machine learning models more interpretable. Although a great deal of effort goes into developing techniques for interpreting the interactions learned by a given model, fewer studies focus on assessing the quality of such explanations. Even fewer focus on how to adjust the model to produce explanations faithful to prior knowledge, a process known as explanation-guided learning. Furthermore, most approaches in this area focus on classification problems and usually assume prior knowledge about which input features or regions are most important. In this work, we introduce a new approach to steering neural networks based on partial dependence, such that their average response to certain features aligns with specific functional domain knowledge about the problem. We empirically demonstrate on a range of regression problems, including dynamical systems forecasting, that models whose training has been controlled using our method perform better than unconstrained models and are more data-efficient. Moreover, we highlight that interpretations obtained from the former actually align with the user-provided knowledge, whereas those obtained from the latter do not.