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.

翻译：算法决策支持（ADS）利用基于机器学习的AI，正成为许多流程的重要组成部分。组织引入ADS以改进决策并利用现有数据，从而可能限制偏离规范性“经济人”及人类决策中的偏差。然而，对组织环境中ADS系统开发与使用的深入观察揭示，它们必然涉及一系列大量未明确的人类决策。这些决策始于考虑哪些决策适合使用ADS，延续至开发与部署ADS过程中的选择，最终以如何将ADS输出应用于组织运营的决策告终。本文概述了这些决策及一些相关的行为现象，并指出了进一步研究的方向——这对于正确评估相关流程及其脆弱性至关重要。理解这些行为方面对于在组织中成功实施ADS具有重要意义。