AI for Government and Policy

Our research examines the organisational challenges involved in adopting AI technologies in government contexts, and strategies to facilitate effective implementation. Through case studies of AI collaborations, we identify key barriers like data limitations, skills gaps, and cultural resistance. We find that while technical challenges often arise during implementation, long-term leadership backing and stakeholder engagement are crucial. Strategies like training, establishing data standards, and formalising data sharing can enable organisations to overcome hurdles.

Additionally, we investigate applying MLOps best practices for machine learning operations to boost reliability, governance, and agility of public sector AI systems. MLOps principles like continuous monitoring, automated workflows, and streamlined collaboration can aid public organisations in deploying complex AI systems despite constraints around skills, resources, and technical debt. Our work provides practical insights into the nuances of embedding AI in public services, highlighting the importance of both technical and managerial considerations. We collaborate closely with government partners to ensure our findings translate into impactful guidance for navigating the AI adoption journey.

Our research explores responsible and ethical integration of AI systems in public service delivery. We develop computational methods for mining citizen feedback to improve services. However, realising AI's promise requires careful management of challenges around public perceptions, workforce impacts, bias, transparency, and legal regulations. Our work examines how to ensure fairness, accountability, and human oversight in public sector AI while delivering benefits like efficiency and personalisation. We aim to demonstrate AI's value for augmenting public services while upholding public trust. Our findings provide policymakers with practical evidence to inform principled AI adoption.

Our research investigates applications of AI to enhance government decision-making and policy outcomes. We explore uses like optimising operations, pattern recognition in large datasets, and predictive analytics. However, the policy context poses distinct ethical and validity requirements compared to commercial AI. Our work examines responsible design of AI systems to support public goals while avoiding bias, opacity, and over-automation. We provide guidance on issues ranging from algorithmic accountability to public sector data limitations to cooperation on global AI policy. Our aim is to ensure AI strengthens governance capabilities in the public interest. We partner with policymakers to translate our interdisciplinary research into actionable insights. 

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.