Human-Machine Collaboration in Decision-Making

Authors

  • Alexander Hatz
  • Jurij Jaklič
  • Frank Morelli Hochschule Pforzheim

DOI:

https://doi.org/10.26034/lu.akwi.2022.3338

Keywords:

Machine Learning, Human-Machine Collaboration, Collaborative Intelligence, Success Factors

Abstract

The use of ML-based decision support systems in business-related decision-making processes is a proven approach for companies to increase process performance and quality. To a certain extent, machines are capable of reproducing the cognitive abilities of humans in specific domains. In order to leverage the resulting potential, synergistic human-machine collaboration (HMC) is becoming increasingly important for companies. However, orchestrating HMC is dependent on a set of framework conditions that determine the success of the collaboration. This study examines the research question of how to utilize the concept of collaborative intelligence (CI) to enhance decision-making processes while using machine learning (ML) -based data prediction. The purpose is to identify success factors in the development, design, and implementation of an ML-based predictive analytics solution to orchestrate HMC in decision-making processes. These success factors state recommendations for companies to fulfil the necessary framework conditions for synergetic HMC orchestration. In total, five success factors were identified that represent a combination of theoretical findings and empirical insights. At the same time, further research needs were uncovered, which point out starting points for future research projects in the field of HMC.

 

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Published

2022-07-10

Issue

Section

Practice