Designing Explainable Predictive Machine Learning Artifacts: Methodology
  and Practical Demonstration
Giacomo Welsch Peter Kowalczyk
Prediction-oriented machine learning is becoming increasingly valuable to
organizations, as it may drive applications in crucial business areas. However,
decision-makers from companies across various industries are still largely
reluctant to employ applications based on modern machine learning algorithms.
We ascribe this issue to the widely held view on advanced machine learning
algorithms as "black boxes" whose complexity does not allow for uncovering the
factors that drive the output of a corresponding system. To contribute to
overcome this adoption barrier, we argue that research in information systems
should devote more attention to the design of prototypical prediction-oriented
machine learning applications (i.e., artifacts) whose predictions can be
explained to human decision-makers. However, despite the recent emergence of a
variety of tools that facilitate the development of such artifacts, there has
so far been little research on their development. We attribute this research
gap to the lack of methodological guidance to support the creation of these
artifacts. For this reason, we develop a methodology which unifies
methodological knowledge from design science research and predictive analytics
with state-of-the-art approaches to explainable artificial intelligence.
Moreover, we showcase the methodology using the example of price prediction in
the sharing economy (i.e., on Airbnb).