Decision-making framework with double-loop learning through interpretable black-box machine learning models
Abstract
Purpose
The purpose of this paper is to address the problem of weak acceptance of machine learning (ML) models in business. The proposed framework of top-performing ML models coupled with general explanation methods provides additional information to the decision-making process. This builds a foundation for sustainable organizational learning.
Design/methodology/approach
To address user acceptance, participatory approach of action design research (ADR) was chosen. The proposed framework is demonstrated on a B2B sales forecasting process in an organizational setting, following cross-industry standard process for data mining (CRISP-DM) methodology.
Findings
The provided ML model explanations efficiently support business decision makers, reduce forecasting error for new sales opportunities, and facilitate discussion about the context of opportunities in the sales team.
Research limitations/implications
The quality and quantity of available data affect the performance of models and explanations.
Practical implications
The application in the real-world company demonstrates the utility of the approach and provides evidence that transparent explanations of ML models contribute to individual and organizational learning.
Social implications
All used methods are available as an open-source software and can improve the acceptance of ML in data-driven decision making.
Originality/value
The proposed framework incorporates existing ML models and general explanation methodology into a decision-making process. To the authors’ knowledge, this is the first attempt to support organizational learning with a framework combining ML explanations, ADR, and data mining methodology based on the CRISP-DM industry standard.
Keywords
Acknowledgements
The authors are grateful to the company Salvirt, Ltd, for funding the research and development presented in this paper. Mirjana Kljajić Borštnar and Marko Robnik-Šikonja were supported by the Slovenian Research Agency, ARRS, through research programs P5-0018 and P2-0209, respectively.
Citation
Bohanec, M., Robnik-Šikonja, M. and Kljajić Borštnar, M. (2017), "Decision-making framework with double-loop learning through interpretable black-box machine learning models", Industrial Management & Data Systems, Vol. 117 No. 7, pp. 1389-1406. https://doi.org/10.1108/IMDS-09-2016-0409
Publisher
:Emerald Publishing Limited
Copyright © 2017, Emerald Publishing Limited