The Challenges of Organizational Factors in Collaborative Artificial Intelligence Projects in the Public Sector

Published in Social Science Computer Review, 2020

Recommended citation: Averill Campion, Mila Gasco, Slava Jankin Mikhaylov, and Marc Esteve. "The Challenges of Organizational Factors in Collaborative Artificial Intelligence Projects in the Public Sector." Social Science Computer Review, forthcoming.

Despite their current popularity and the steady increase in the number of articles published over time, research on artificial intelligence (AI) in public contexts is still scarce, and assumptions on the drivers, challenges, and impacts of AI in government are still far from conclusive. By using a comparative case study between a large research university in England and two different county councils involved in a multi-year collaborative project around AI, we study the challenges of interorganizational collaborations in the development of AI tools and the implementation of managerial routines used to address those challenges. Our results show that resistance to share the data, which is the result of privacy/security concerns, a lack of understanding of what data was available/needed, a lack of alignment between project interests and expectations around data sharing, and a lack of engagement throughout the organizational hierarchy, are the most important challenges. Further, organizational routines to overcome challenges include working on-site, showing the benefits of data sharing, re-framing problems, designating joint appointments and boundary spanners, and connecting all levels of collaborative participants around project design and purpose.