  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 use available data, thereby possibly limiting deviations
from the normative "homo economicus" and the biases that characterize human
decision-making. However, a closer look at the development and use of ADS
systems in organizational settings reveals that they necessarily involve a
series of largely unspecified human decisions. They begin with deliberations
for which decisions to use ADS, continue with choices while developing and
deploying the ADS, and end with decisions on how to use the ADS output in an
organization's operations. The paper presents an overview of these decisions
and some relevant behavioral phenomena. It points out directions for further
research, which is essential for correctly assessing the processes and their
vulnerabilities. Understanding these behavioral aspects is important for
successfully implementing ADS in organizations.
