Automated Triage of Complaints and Appeals with AI in Municipalities

Why automated triage helps citizen services

Municipalities face a growing flow of complaints, claims and administrative appeals. An AI-based triage system can cut response times, prioritize urgent matters (safety, health risk) and assign cases to the correct department. This isn’t about delegating legal decisions to a model: it’s a classification and management support tool so staff can focus effort where it has the most impact.

Concrete benefits:

• Reduced time to assign cases.
• Early detection of urgent or recurring cases.
• Improved SLAs and regulatory compliance through correct routing.
• Structured records for later analysis (bottlenecks, recurrent issues).

Key risks and essential regulatory framework

Before deploying any solution, evaluate operational and legal risks:

• Data protection: comply with the GDPR (data minimization, legal bases, and data subject rights such as access, rectification, erasure and objection). Design flows that avoid exposing sensitive data to external models.
• Security: comply with the ENS (Royal Decree 311/2022) for services that process public administration information.
• Transparency and AI Act obligations: assess whether the system falls within the scope of the regulation (for example, when classification affects rights) and prepare the necessary documentation and risk assessments.
• Bias and classification errors: risk of mis-prioritizing or creating discriminatory outcomes in service delivery.

Practical design: minimum steps for reliable triage

1. Define objectives and functional limits

• What will be automated? (topic classification, priority by urgency, duplicate detection).
• Decide exclusions: appeals with suspensive effect, claims outside municipal competence, or matters that always require human intervention.

2. Taxonomy and operational tags

• Create a short, practical category catalog (e.g. public safety, social services, public space, tax, duplicate).
• Define objective priorities: risk to people, imminent legal deadline, collective impact.

3. Data and preparation

• Use anonymized historical records to train classifiers. Maintain an SBOM (Software Bill of Materials) for models and datasets.
• Avoid using sensitive data without clear legal bases and appropriate pseudonymization measures.

4. Model and architecture

• Start with lightweight supervised classifiers (fine-tuned LLMs with controls, or traditional NLP models) and heuristic rules (keywords, urgency expressions).
• Design integration with the document management system (DMS)/electronic case file so cases can be created or updated automatically.

5. Human supervision (human-in-the-loop)

• The model suggests category and priority; an employee validates in the early phases.
• Set confidence thresholds: below X% the routing is manual.

6. Logging, traceability and transparency

• Record the model's suggestion, the human decision and the model version (model card). This facilitates auditing and AI Act compliance.
• Inform the claimant, when appropriate, about the use of AI in case handling (minimum transparency).

7. Monitoring and metrics

• Operational metrics: average assignment time, human validation rate, recall/precision per category, false negatives on urgent cases.
• Compliance metrics: number of data accesses, access audits, security incidents.

Essential technical and organizational controls

• Restricted access and logging in line with the ENS.
• Masking/pseudonymization when sharing data for training.
• Impact assessment (AIA) if the system can significantly affect rights; documentation required by the AI Act.
• AI incident response plan: fallback to manual process and internal notification.

Quick operational example (minimum viable)

1. Input: email/portal/phone → OCR/NLP extracts text.
2. Classifier returns: {category: "public space", priority: "high", confidence: 0.87}.
3. Rule: if priority is high → notify technical services and generate a provisional case.
4. Employee validates in review queue: accept/adjust category.
5. System logs model version, decision and time to assignment.

Integration and secure deployment

• Pilot for 60–90 days on a single channel (for example, the complaints portal).
• Weekly review of automated decisions and taxonomy adjustments.
• Clear SLAs with the provider for model updates, audits and data deletion.

On modular platforms like OptimGov it’s possible to accelerate this integration while maintaining security controls, but organizational and legal design must always precede technical implementation.

Takeaway — Immediate recommended action

Run a controlled pilot: pick one channel, define 6–8 operational categories, use anonymized historical data to train a simple classifier and set a human validation threshold. Document the process (decision logs and model version), verify GDPR and ENS compliance, and prepare an AIA if the scope could affect rights. A pilot like this lets you measure real impact on response times with limited risk.