With the increasing prevalence of Machine Learning in everyday life, a growing number of people will be provided with Machine-Learned assessments on a regular basis. We believe that human users interacting with systems based on Machine-Learned classifiers will demand and profit from the systems’ decisions being explained in an approachable and comprehensive way. We developed a general process framework for logic-rule-based classifiers facilitating mutual exchange between system and user. The framework constitutes a guideline for how a system can apply Inductive Logic Programming in order to provide comprehensive explanations for classification choices and empowering users to evaluate and correct the system’s decisions. It also includes users’ corrections being integrated into the system’s core logic rules via retraining in order to increase the overall performance of the human-computer system. The framework suggests various forms of explanations—like natural language argumentations, near misses emphasizing unique characteristics, or image annotations—to be integrated into the system.

随着日常生活中机器学习的普及，越来越多的人将定期获得机器学习评估。我们认为，人类用户与基于机器学习的分类器的系统进行交互将需要以系统，全面的方式对系统的决策进行解释并从中受益。我们为基于逻辑规则的分类器开发了一个通用的过程框架，以促进系统和用户之间的相互交换。该框架构成了系统如何应用归纳逻辑编程的指南，以便为分类选择提供全面的解释，并使用户能够评估和更正系统的决策。它还包括通过重新培训将用户的更正集成到系统的核心逻辑规则中，以提高人机系统的整体性能。该框架建议将各种形式的解释（例如自然语言论证，强调独特特征的遗漏或图像注释）集成到系统中。