TY  - JOUR
U1  - Wissenschaftlicher Artikel
A1  - Sankaran, Ganesh
A1  - Palomino, Marco A.
A1  - Knahl, Martin
A1  - Siestrup, Guido
T1  - A Modeling Approach for Measuring the Performance of a Human-AI Collaborative Process
JF  - Applied Sciences
N2  - 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.
KW  - Machine learning
KW  - System dynamics
KW  - Simulation modeling
KW  - Algorithmic decision-making
KW  - Supply chain planning
Y1  - 2022
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:fn1-opus4-90522
SN  - 2076-3417
SS  - 2076-3417
U6  - https://doi.org/10.3390/app122211642
DO  - https://doi.org/10.3390/app122211642
VL  - 12.2022
IS  - 22
SP  - 26
S1  - 26
ER  -