Machine Learning Informed Decision-Making with Interpreted Model's Outputs

Abstract

Despite having set the theoretical ground for explainable systems decades ago, the information system scholars have given little attention to new developments in the decision-making with humans-in-the-loop in real-world problems. We take the sociotechnical system lenses and employ mixed-method analysis of a field intervention to study the machine-learning informed decision-making with interpreted models' outputs. Contrary to theory, our results suggest a small positive effect of explanations on confidence in the final decision, and a negligible effect on the decisions' quality. We uncover complex dynamic interactions between humans and algorithms, and the interplay of algorithmic aversion, trust, experts' heuristic, and changing uncertainty-resolving condititions.