The field of artificial intelligence (AI) has yet to fully capitalise on the potential to develop AI systems through collaboration between system developers and data scientists, especially when they belong to different organisations. We studied how using a marketplace creates value in such value chains by allowing organisations to advertise and share AI assets like data and models and enable multi-party development of AI systems while protecting these assets. The paper describes an embedded multi-case study of a marketplace under development, the Bonseyes marketplace. The cases were a collaboration between universities, a large company outsourcing data science, and a small business offering AI models as products. Value creation was linked to reduced development time, streamlined product communication, and protection of shared assets. However, participants required data protection concerns to be addressed, as well as marketplace maturity. The marketplace created value with clear goals for developing an AI system and supporting tools, templates, and examples. Our study benefits researchers wanting to advance AI systems development by offering rich examples of how a marketplace can enable multi-party collaborations.

Platforms are emerging that allow data scientists, software and hardware engineers to collaborate through organisational boundaries to develop systems of Artificial Intelligence (AI). Such collaboration involves the exchange of assets representing Intellectual Property (IP) of the collaborators. The tension between permitting access and protecting IP is thus one of the critical challenges faced by organisations willing to innovate through collaboration. Licensing is a common way to address the issue, but the influence of the licensing rules on the intended form of collaboration is still unclear.In this paper, we identify and analyse the rules that are used to regulate IP exchanges in two common forms of collaboration: a) competitions where one customer benchmarks and selects among multiple suppliers and b) consortium projects where multiple parties collaborate to product joint results. Due to our interest in AI, we have chosen to analyse the terms and conditions of competitions hosted on KaggleTM a leading online platform for Competitions. For consortium projects, we have analysed the DESCA Consortium Agreement template. DESCA is often used for European projects, an increasing number of which are used to fund AI innovation projects. We have applied In Vivo Coding and Concept Coding coding techniques to highlight rules applicable to IP exchange. We structured the findings in the form of tree graphs consisting of interdependent textual phrases to extract, group and compare the terms and conditions of IP sharing in each collaboration form and how they relate to the characteristics of the studied collaborations.The results indicate that each form of collaboration has its own set of rules that address comparable concerns but have different content. Practitioners, both platform providers and collaborators, can utilise our results to implement licensing for IP exchange that fits the desired type of collaboration. For researchers, our results represent a step towards the automation of license generation and enforcement. © 2020 IEEE.