Accelerating smartly - how AI helps municipal officials with simple casuistry in the social domain

What is simple casuistry?

Simple case histories are characterized by clearly applicable laws and regulations, established policy frameworks and known case law. The applicant's situation hardly differs from previous cases. Consider a resident with mobility problems who applies for a wheelchair based on the Wmo 2015, while there are no special medical conditions or alternative provisions. The decision maker immediately recognizes the pattern and tests the application against clear criteria. The policy maker also has crystallized policies in these cases that raise few questions.

AI as an accelerator for decision makers

Decision makers process dozens of applications every day with largely predictable elements. AI systems can support them in this process. For example, a well-trained system analyzes previous decisions, recognizes patterns in data and compares the new application with previous, similar situations. Based on that, the system automatically generates a draft decision for the decision maker to review and approve.

By having the system perform routine checks, such as testing against standard criteria or retrieving relevant policies, decision makers save time. Instead of constantly going through the entire legal framework, they focus on anomalies, risks or signals that require additional attention.

AI as an accelerator for policymakers

Policymakers also benefit from AI on simple policy issues. Consider updating policy rules for household assistance. AI tools analyze existing texts, identify inconsistencies, and make suggestions for improvement or standardization. When a policy maker needs to revise policy documents following a change in the law, a language model can provide relevant articles of law and automatically check whether the existing text is consistent with them.

AI also speeds up the preparation of memoranda or council proposals. By analyzing previous texts, the system compiles relevant passages. The policy maker only needs to check, supplement or rewrite them. This prevents the official from repeating work and increases the consistency of policy documents within the organization.

General example: home modification support

A resident applies for a shower chair through the Wmo. The municipality has clear policy rules for such requests, with standard criteria on medical necessity, prior facilities and efficiency. In this situation, an AI system compares the applicant's data with previously approved cases and prepares a draft decision on that basis.

The decision maker does not have to go through law books or policy frameworks, but checks the consistency and fairness of the proposed text. The policy maker, who is responsible for the annual update of the "housing adjustment" policy framework, can also use AI to quickly see which texts require adjustment.

AI does not replace humans, but supports targeted

AI does not make decisions or establish policy in this context. The official always retains responsibility for the content and legal quality of the decision or policy document. AI acts as an accelerator by providing standard information, reducing repetitive work, and making suggestions based on previous work. The human professional assesses the relevance of those suggestions and makes adjustments as needed.

Increase in work quality through speed

Acceleration in simple cases provides room for deepening elsewhere. Civil servants have more time left for citizens with complex problems or for issues where standard rules fall short. By cleverly deploying AI, they not only increase the speed of their work, but also the quality of their services.

Looking ahead: from accelerating to improving

In simple cases, the emphasis is on speed and efficiency. In complicated cases, where legislation is more extensive and application more complicated, AI actually helps improve quality and completeness. More on that in the next blog of this trilogy.