Interpretable data science for decision making

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.