Algorithmic decision support (ADS), using Machine-Learning-based AI, is becoming a major part of many processes. Organizations introduce ADS to improve decision-making and make optimal use of data, thereby possibly avoiding deviations from the normative "homo economicus" and the biases that characterize human decision-making. A closer look at the development process of ADS systems reveals that ADS itself results from a series of largely unspecified human decisions. They begin with deliberations for which decisions to use ADS, continue with choices while developing the ADS, and end with using the ADS output for decisions. Finally, conclusions are implemented in organizational settings, often without analyzing the implications of the decision support. The paper explores some issues in developing and using ADS, pointing to behavioral aspects that should be considered when implementing ADS in organizational settings. It points out directions for further research, which is essential for gaining an informed understanding of the processes and their vulnerabilities.

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