Citizen safety in governmental AI-supported decision-making

Abstract

Governmental AI-supported decision-making is paramount when impacting citizens. Citizens are subject to their government’s decision-making, which is crucial when they transact, as examining the transaction’s rightfulness is executed by the same government. Thus, control and monitoring are evident, which are increasingly applied through AI-supported tools (Hoekstra et al., 2021). It is deemed socially unacceptable when government falsely accuses their citizens of unrightful transactions. Vice versa, it is also deemed unacceptable when criminals taking advantage of public money are exonerated. The citizens whom are trapped in these systems, like the benefits system, are often vulnerable and have low incomes. The Dutch government wrongly accused 20.000 parents of fraud, resulting in major con-
sequences. Therefore, research is crucial for both a better functioning government and protecting the safety of vulnerable citizens. Scientific relevance is emphasised by the narrow understanding of how harm of next similar case is prevented, as such systems are barely researched holistically. Limited
understanding of the relations within and between socio-technological contexts and the safekeeping of citizens lead to the main question:

“How can citizen’s safety be safeguarded in governmental AI-supported decision-making?”

Design science is the main methodology, consisting of three cycles: the rigour cycle, relevance cycle, and design cycle (Hevner, 2014). The method allows for innovative thinking and combining empirical (relevance) and scientific (rigour) knowledge toward exploring a new solution, in this research, safeguarding citizen’s safety (design). Additionally, it allows for a system’s approach required to understand the relationships of different decision-making components and for combining empirical and scientific insights (vom Brocke et al., 2020). Combining this with a holistic perspective, the research incorporates strategical, tactical, and operational challenges and specifically including the political dimension; more than factors and actors. Insight is created between the different context, which are all crucial in establishing a well balanced system.
In seeking an answer to the main question, three parts are crucial: the system of AI-supported decision-making, the concept of citizen’s safety and how both can be integrated. The system and its boundaries are discovered through scientific and empirical exploration, serving as a framework. The definition of citizen’s safety and its implications are discovered by relating to the system and its characteristics. With these two parts, the influence of the system on citizen’s safety is explored and leads to the third crucial part, validated by a serious game. All parts use different sub-methodologies to obtain
the required knowledge for the design science methodology.