Designing Explainable Predictive Machine Learning Artifacts: Methodology and Practical Demonstration
This work addresses the reluctance of business decision-makers to use machine learning by providing methodological guidance for creating explainable predictive artifacts, though it is incremental as it builds on existing tools and approaches.
The paper tackles the adoption barrier of machine learning due to its 'black box' nature by developing a methodology that integrates design science, predictive analytics, and explainable AI, demonstrated through a price prediction case study on Airbnb.
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).