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.

翻译：算法决策支持（ADS）借助基于机器学习的AI，正在成为众多流程的重要组成部分。组织引入ADS是为了改进决策制定，优化数据利用，从而可能避免偏离规范化的“经济人”假设以及人类决策中所特有的偏差。然而，对ADS系统开发过程的深入审视揭示出，ADS本身源于一系列在很大程度上未经明确界定的人类决策。这些决策始于关于哪些决策环节应使用ADS的考量，贯穿于开发ADS过程中的选择，并终结于利用ADS输出结果进行决策的环节。最终，结论在组织环境中得到实施，而决策支持的影响往往未经分析。本文探讨了开发与使用ADS过程中的若干问题，指出了在组织环境中实施ADS时应考虑的行为因素。文章同时指明了进一步研究的方向，这对于深入了解相关流程及其脆弱性至关重要。