The disruptive potential of AI systems roots in the emergence of big data. Yet, a significant portion is scattered and locked in data silos, leaving its potential untapped. Federated Machine Learning is a novel AI paradigm enabling the creation of AI models from decentralized, potentially siloed data. Hence, Federated Machine Learning could technically open data silos and therefore unlock economic potential. However, this requires collaboration between multiple parties owning data silos. Setting up collaborative business models is complex and often a reason for failure. Current literature lacks guidelines on which aspects must be considered to successfully realize collaborative AI projects. This research investigates the challenges of prevailing collaborative business models and distinct aspects of Federated Machine Learning. Through a systematic literature review, focus group, and expert interviews, we provide a systemized collection of socio-technical challenges and an extended Business Model Canvas for the initial viability assessment of collaborative AI projects.

翻译：AI系统的颠覆性潜力源于大数据的涌现。然而，大量数据分散并锁定在数据孤岛中，致使其潜力未能得到开发。联邦机器学习是一种新型AI范式，能够从分散、可能孤立的去中心化数据中构建AI模型。因此，联邦机器学习在技术上可打破数据孤岛，从而释放经济潜力。但这需要拥有数据孤岛的多方主体开展协作。构建协作型商业模式复杂且常是失败的原因。当前文献缺乏关于成功实现协作AI项目必须考虑哪些方面的指导。本研究调查了现有协作型商业模式与联邦机器学习独特方面的挑战。通过系统文献综述、焦点小组和专家访谈，我们提供了社会技术挑战的系统化集合，以及用于协作AI项目初始可行性评估的扩展商业模式画布。