Designing Explainable Predictive Machine Learning Artifacts: Methodology and Practical Demonstration

Machine learning (ML) is a focal element of digitization that affects many areas of modern society: besides driving a plethora of physical and virtual products already woven into our daily lives, such as smartphones and social media platforms, ML techniques can be leveraged to power a wide range of business applications [1, 2]. Although ML as an umbrella term comprises various techniques, some of which are aimed at different purposes, most ML algorithms are designed to calculate empirical predictions based on given data [2]. This prediction-oriented approach to ML is widely referred to as supervised learning, predictive analytics, or predictive modeling, and initially requires at least two data sets: one for model training and one for testing [2, 3]. While the former allows a given ML algorithm to "learn" patterns that connect the model input and output, the latter serves to evaluate the predictive accuracy of a trained model. In practice, if a corresponding ML model is attributed to possess a sufficient degree of predictive power, it may be deployed in a productive environment to compute real-world predictions, e.g., to support managerial decision making. The application of supervised learning in business contexts is highly relevant as it may drive applications in the fields of predictive maintenance, financial fraud detection, personalized product recommendation, and more. Consequently, the global ML market size was valued at US$ 34.56 billion in 2021 and is expected to grow to US$ 74.99 billion by 2028 at a compound annual growth rate of 25.7% [4]. Given the enormous business potential of ML, a considerable number of companies have already begun to launch data analytics initiatives to automate their processes or support their decision making over the last years.