This project examines strategies for governments to develop in-house expertise in data science and AI, reducing dependence on external providers. Our research shows relying solely on private sector partnerships risks privacy breaches, unaccountable systems, and solutions lacking policy expertise.
We identify core competencies like machine learning, causal analysis, and agile development. Capacity building approaches we study include adapting recruitment and training, establishing communities of practice, collaborating with research institutions, holding competitions and hackathons, and balancing centralisation with wider diffusion.
Our findings provide actionable recommendations for sustainably growing public sector data science and AI talent. We advise on attracting experts through improved hiring practices and work conditions. We highlight the value of networks for sharing best practices and intersectoral learning.
Overall, this project aims to enable governments to harness data science and AI effectively and responsibly. Our guidance helps translate these technologies' potential into real improvements in service delivery, regulation, and policymaking for the public good. We partner with governments to implement evidence-based strategies for digital transformation.