Making better decisions - how AI helps municipal officials with complicated casework in the social domain
In the social domain, decision makers and policy makers regularly review case histories that are more complicated than standard cases, but still within their reach. These complicated casuistries require detailed knowledge of legislation, municipal policy and case law. AI helps officials in such situations not so much by speeding up, but rather by deepening and improving their work. In this blog, I explain how AI acts as a knowledge support system in these multilayered cases.
What is complicated casuistry?
Complicated casuistry requires in-depth knowledge of a multitude of legal sources. Think of situations in which different legal frameworks coincide, or in which judgments of the Central Appeals Council give a different interpretation than municipal policy had previously prescribed. The civil servant can solve the case substantively, but to do so he must remain legally sharp, recognize exceptions and have recent case law at hand. In practice, this is not always possible. Laws and regulations change quickly and it is almost impossible to keep all relevant information up to date.
AI as an enhancer for decision makers
A decision-maker in the social domain tests applications against laws, municipal policy regulations, and sometimes administrative court rulings. In complicated cases, such as a young person with a mild intellectual disability applying for an individual provision based on the Youth Act, the decision-maker must check which legal regime applies, whether there are any subsidiary provisions and whether the application fits within municipal policy.
AI can help by providing access to relevant legal articles, internal policy documents and recent case law. Suppose a parent objects to a rejected application for youth aid because another municipality does grant aid under similar circumstances. In that case, an AI system can display relevant CRvB decisions addressing those differences. This provides the decision-maker with broader context and reasoning and prevents unintentional legal inequality.
AI can also signal that the policy rules are no longer consistent with applicable case law. Thus, the system not only strengthens the substantive quality of the decision, but also prevents legal risks for the municipality.
AI as an enhancer for policymakers
Policymakers in the social domain regularly struggle with the question of whether their policy frameworks are still in line with the current interpretation of the law. For example: the Participation Act contains strict rules for the cost-sharing standard, but case law gradually introduces nuances concerning family caregivers or temporary residence. An AI assistant can signal that recent judgments call into question the strict application of the cost-sharing standard.
By juxtaposing policy documents with current case law, AI makes visible where policies need updating. In addition, AI helps structure policy changes: the system recognizes parallels with other policies, refers to relevant passages from legislative history or policy memoranda, and makes suggestions for wording that is more consistent with common legal interpretations.
General example: multi-problem assistance under the Wmo
A single parent with mental health issues and debt applies for support through the Wmo. The application touches several policy domains: debt assistance, social support and possibly youth assistance for the children. The decision maker must find out whether the Wmo provides a sufficient basis, whether cooperation with other domains is necessary, and whether existing services provide sufficient coverage. AI helps by collecting relevant legal articles, analyzing policy notes on cross-domain support, and understanding previous similar cases.
The policymaker, who is responsible for integrated support policies, uses AI to align local policies with broader legislation and recent court decisions on cross-domain cooperation. In this way, the policymaker strengthens the legal tenability and enforceability of policies.
AI fills knowledge gaps, but does not replace craftsmanship
AI provides overview, sources, connections and suggestions. Yet interpretation remains human work. The decision-maker weighs interests, assesses individual circumstances and ultimately makes the decision. The policy maker translates legal insights into local policy, in consultation with government, residents and chain partners. AI supports, but does not decide or write independently.
Strategic advantage: fewer errors, better substantiation
AI strengthens the professionalism of officials in complicated casework. By identifying knowledge gaps and providing legal insights, the system increases the quality and consistency of decisions and policies. Officials reduce the chances of objection, appeal and annulment, and provide legal certainty to citizens based on up-to-date and applicable information.
Looking ahead: dealing with uncertainty
In complicated casuistry, the emphasis is on completeness and precision. In complex casuistry, the focus is on something else: uncertainty. There, rules are missing, sources contradict each other or officials have to make choices without a clear framework. In that context, AI helps not by improving, but by
predict. In the third blog, I discuss how AI generates scenarios to support decision-making in uncertain situations.
Read the first part of this series here:
Speeding up smartly - on how AI helps municipal officials with simple casuistry in the social domain.
