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，大部分数据分散并锁于数据孤岛中，其潜力未被开发。联邦机器学习是一种新型AI范式，能够从分散且可能孤岛化的数据中创建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。但这要求多个持有数据孤岛的参与方协作。建立协作商业模式复杂且常导致失败。当前文献缺乏关于成功实现协作AI项目所需考虑因素的指南。本研究探讨了现有协作商业模式及联邦机器学习独特方面的挑战。通过系统性文献综述、焦点小组和专家访谈，我们系统化地整理了社会技术挑战集合，并扩展了商业模式画布，用于协作AI项目的初始可行性评估。