For a long time, machine learning (ML) has been seen as the abstract problem of learning relationships from data independent of the surrounding settings. This has recently been challenged, and methods have been proposed to include external constraints in the machine learning models. These methods usually come from application-specific fields, such as de-biasing algorithms in the field of fairness in ML or physical constraints in the fields of physics and engineering. In this paper, we present and discuss a conceptual high-level model that unifies these approaches in a common language. We hope that this will enable and foster exchange between the different fields and their different methods for including external constraints into ML models, and thus leaving purely data-centric approaches.

翻译：长期以来，机器学习（ML）被视为从数据中学习关系而与周围环境无关的抽象问题。这一观点近期受到挑战，已有研究提出将外部约束纳入机器学习模型的方法。这些方法通常源于特定应用领域，例如机器学习公平性领域的去偏算法，或物理与工程领域的物理约束。本文提出并讨论了一个概念性高层模型，该模型以统一语言整合了这些方法。我们希望该模型能促进不同领域及其将外部约束纳入机器学习模型的不同方法之间的交流与融合，从而推动摆脱纯粹的数据驱动方法。