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A Modeling Approach for Measuring the Performance of a Human-AI Collaborative Process

  • Despite the unabated growth of algorithmic decision-making in organizations, there is a growing consensus that numerous situations will continue to require humans in the loop. However, the blending of a formal machine and bounded human rationality also amplifies the risk of what is known as local rationality. Therefore, it is crucial, especially in a data-abundant environment that characterizes algorithmic decision-making, to devise means to assess performance holistically. In this paper, we propose a simulation-based model to address the current lack of research on quantifying algorithmic interventions in a broader organizational context. Our approach allows the combining of causal modeling and data science algorithms to represent decision settings involving a mix of machine and human rationality to measure performance. As a testbed, we consider the case of a fictitious company trying to improve its forecasting process with the help of a machine learning approach. The example demonstrates that a myopic assessment obscures problems that only a broader framing reveals. It highlights the value of a systems view since the effects of the interplay between human and algorithmic decisions can be largely unintuitive. Such a simulation-based approach can be an effective tool in efforts to delineate roles for humans and algorithms in hybrid contexts.

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Author:Ganesh Sankaran, Marco A. Palomino, Martin KnahlGND, Guido SiestrupGND
Parent Title (English):Applied Sciences
Document Type:Article (peer-reviewed)
Year of Completion:2022
Release Date:2023/01/16
Tag:Algorithmic decision-making; Machine learning; Simulation modeling; Supply chain planning; System dynamics
Article Number:11642
Page Number:26
Open-Access-Status: Open Access 
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International