Inter-Organizational Collaborative Machine Learning: A Problem Space Exploration

Abstract

Organizations can benefit substantially from machine learning (ML), but individual organizations often encounter resource constraints (e.g., related to data and computational resources) when using ML. Inter-organizational collaboration represents a promising approach to overcoming such resource constraints. While there are various inter-organizational collaborative ML solutions, we lack a thorough understanding of the corresponding problem space. Based on a structured literature review and qualitative content analysis, we explore the problem space of inter-organizational collaborative ML and uncover six stakeholders, six needs, seven goals, and seven requirements. We also identify four promising future research directions. Our study lays a foundation for developing design knowledge and more targeted solutions for inter-organizational collaborative ML.

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