This paper describes the foundations of interpretable data science for decision making and serves as an editorial to the corresponding special issue. Interpretable data science analyzes data that summarizes domain relationships to produce knowledge that is readily understandable by human decision makers. To this end, we contextualize the current role of interpretable data science for improved business decision making and introduce the notion of an interpretable decision support system (iDSS). We discuss five underlying characteristics of iDSS, i.e., performance, scalability, comprehensibility, justifiability and actionability. This paper further zooms in on pertinent data science decisions in the input, processing and output stage when designing iDSS. For each of the contributing papers in this special issue, we describe their major contributions to the field of interpretable data science for decision making.

本文描述了用于决策的可解释数据科学的基础，并作为相应特刊的社论。可解释数据科学分析总结领域关系的数据，以产生人类决策者容易理解的知识。为此，我们将可解释数据科学在改进业务决策方面的当前作用置于上下文中，并引入可解释决策支持系统 (iDSS) 的概念。我们讨论了 iDSS 的五个基本特征，即性能、可扩展性、可理解性、正当性和可操作性。本文进一步放大了设计 iDSS 时输入、处理和输出阶段的相关数据科学决策。对于本期特刊中的每篇贡献论文，