@article{JankinEsteveCampion, author = {Jankin, Slava and Esteve, Marc and Campion, Averill}, title = {Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration}, series = {Philosophical Transactions of the Royal Society A}, volume = {376}, journal = {Philosophical Transactions of the Royal Society A}, number = {2128}, doi = {10.1098/rsta.2017.0357}, abstract = {Public sector organizations are increasingly interested in using data science and artificial intelligence capabilities to deliver policy and generate efficiencies in high uncertainty environments. The long-term success of data science and artificial intelligence (AI) in the public sector relies on effectively embedding it into delivery solutions for policy implementation. However, governments cannot do this integration of AI into public service delivery on their own. The UK Government Industrial Strategy is clear that delivering on the AI grand challenge requires collaboration between universities and public and private sectors. This cross-sectoral collaborative approach is the norm in applied AI centres of excellence around the world. Despite their popularity, cross-sector collaborations entail serious management challenges that hinder their success. In this article we discuss the opportunities and challenges from AI for public sector. Finally, we propose a series of strategies to successfully manage these cross-sectoral collaborations.}, language = {en} } @article{CampionGascoJankinetal., author = {Campion, Averill and Gasco, Mila and Jankin, Slava and Esteve, Marc}, title = {Managing Artificial Intelligence Deployment in the Public Sector}, series = {Computer/IEEE, 2020}, volume = {53}, journal = {Computer/IEEE, 2020}, number = {10}, doi = {10.1109/MC.2020.2995644}, pages = {28 -- 37}, abstract = {The scarcity of empirical evidence surrounding the organizational challenges and successful approaches to artificial intelligence (AI) deployment has resulted in mostly theoretical conceptualizations. By analyzing policy labs and offices of data analytics across the US to understand organizational challenges of AI adoption and implementation in the public sector as well as to identify successful management strategies to address such challenges, our study moves from speculation to gathering evidence. Our findings show that most challenges are found during the implementation stage and include challenges related to skills, culture, and resistance to share the data driven by data challenges. Further, our results indicate that long term strategies and short term actions need to be put in place to address these challenges. Among the first ones, leadership and executive support and stakeholder management seem to play an important role. Data standardization, training, and data-sharing agreements also seem to be successful specific short-term actions.}, language = {en} }