@article{SankaranPalominoKnahletal.2022,
  author    = {Sankaran, Ganesh and Palomino, Marco A. and Knahl, Martin and Siestrup, Guido},
  title     = {A Modeling Approach for Measuring the Performance of a Human-AI Collaborative Process},
  journal   = {Applied Sciences},
  volume    = {12.2022},
  number    = {22},
  issn      = {2076-3417},
  doi       = {10.3390/app122211642},
  pages     = {11642},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}