As algorithms powered by Artificial Intelligence (AI) are increasingly involved in the management of organizations, it becomes imperative to conduct human-centered AI management research and understand people's feelings and behaviors when machines gain power over humans. The two mainstream methods – vignette studies and case studies – reveal important but inconsistent insights. Here we discuss the respective limitations of vignette studies (affective forecasting errors, biased media coverage, and question substitution) and case studies (social desirability biases and lack of random assignment and control conditions), which may lead them to overrate negative and positive reactions to AI management, respectively. We further discuss the advantages of a third method for mitigating these limitations: field experiments on crowdsourced marketplaces. A proof-of-concept study on Amazon Mechanical Turk (Mturk; as a world-leading crowdsourcing platform) showed unique human reactions to AI management, which were not perfectly aligned with those in vignette or case studies. Participants (N = 504) did not differ significantly under AI versus human management, in terms of performance, intrinsic motivation, fairness perception, and commitment. We suggest that crowdsourced marketplaces can go beyond human research subject pools and become models of AI-managed workplaces, facilitating timely behavioral research and robust predictions on human-centered work designs and organizations.

随着人工智能（AI）驱动的算法越来越多地参与到组织的管理中，开展以人为本的人工智能管理研究并了解当机器超越人类时人们的感受和行为变得势在必行。两种主流方法——小插图研究和案例研究——揭示了重要但不一致的见解。在这里，我们讨论小插图研究（情感预测错误、有偏见的媒体报道和问题替代）和案例研究（社会期望偏差以及缺乏随机分配和控制条件）各自的局限性，这可能导致他们高估负面和正面反应分别是人工智能管理。我们进一步讨论了缓解这些限制的第三种方法的优点：在众包市场上进行现场实验。Amazon Mechanical Turk（Mturk；作为世界领先的众包平台）的一项概念验证研究显示了人类对人工智能管理的独特反应，这与小插图或案例研究中的反应并不完全一致。在人工智能与人类管理下，参与者（N  = 504）在绩效、内在动机、公平感和承诺方面没有显着差异。我们建议，众包市场可以超越人类研究主题库，成为人工智能管理的工作场所的模型，促进及时的行为研究和对以人为中心的工作设计和组织的稳健预测。