In our thesis, we conducted an in-depth exploration of the integration of Artificial Intelligence (AI) in public sector decision-making, focusing particularly on AI case routing for welfare benefits allocation. Initially, our research was guided by a techno-optimistic viewpoint, presuming that municipalities would inherently benefit from AI adoption. However, as our study progressed, we encountered the complexities and challenges inherent in the practical implementation of AI in public services.

We employed the Design Science Research methodology, utilizing a diverse array of methods including literature reviews, case studies, expert interviews, and system safety analysis. Our objective was to develop a method-type artifact to enhance decision-making in civil services, with a special emphasis on the application of AI in welfare benefits allocation. Our findings indicated that AI implementation in the public sector is highly context-dependent, requiring a tailored approach that addresses various technical, organizational, and cultural barriers.

Throughout our research, we identified critical factors such as the need for transparency, fairness, and accountability in AI systems. We recognized the importance of adopting a balanced perspective that considers both AI and non-AI solutions. Our study highlighted the potential benefits of AI in public services, such as increased efficiency and improved quality of decision-making. However, we also noted significant challenges in ensuring system safety and maintaining ethical standards.

Through expert interviews and a detailed system safety analysis, we emphasized the necessity of establishing clear rules for the responsible use of AI case routing and addressing the associated risks. Our research concluded that AI might not always be the most suitable approach, particularly if safety concerns and ethical considerations outweigh the potential benefits.

In summary, our thesis underscores the need for a critical and balanced approach to the integration of AI in public services. We advocate for a holistic strategy that involves collaboration among government, academia, industry, and civil society. This approach is vital to harness the potential benefits of AI while mitigating risks and ensuring the well-being of society.

As stated this research aims to provide insights into the criteria and trade-offs that determine a responsible level of automation when automating decision-making by civil servants through artificial intelligence within smaller public organisations, such as municipalities, in the Netherlands. Making use of expert interviews and vignette experiments in order to provide guidance to those willing to experiment with responsible AI implementation in the public domain.