Transparent decision logic
Every rule and decision branch is visible, inspectable and grounded in policy rather than not a black box. Decision paths are documented and reviewable by authorized stakeholder.
Explainable decision support for Public Administration
Structured reasoning engine for HR, policy & compliance.
Convert administrative rules into transparent, reproducible, and auditable decision processes – ready for oversight, justification, and institutional use.
- Same inputs → same outputs, with complete audit logs.
- Based on administrative science and legal reasoning.
- Designed for HR, policy and compliance teams in public institutions.
Q: Mobility allowance eligibility
1. Check contract type
2. Check distance threshold
3. Check working pattern
4. Check policy references
→ Outcome: Eligible
→ Steps logged for audit
→ Policy reference: MOB-2025-03
→ Audit trail ID: AUDIT-2025-03-15-001
Product
Structured, explainable decision support for institutions.
Built for HR, policy, compliance and administrative teams needing transparency, fairness and auditability – without sacrificing operational speed.
Evidence-based and institutionally validated
Structured on administrative science, legal insight and institutional practice. Models are co-designed with practitioners to ensure practical applicability in institutional contexts.
Reproducible and fair
Same inputs → same outputs, with logs designed for fairness, traceability and internal review. Fairness checks are embedded at every decision point.
Example
Example institutional decision model.
A simplified illustration of how a structured decision proccess operates in an institutional context.
Mobility allowance eligibility decision
Inputs: contract type, distance, work pattern, policy reference.
Output: eligibility decision with full, auditable reasoning chain.
This model illustrates how complex administrative rules can be structured into explicit, auditable steps, ensuring institutional context and readiness for deployment.
1. IF contract_type in {permanent, long_term}
2. AND distance_km ≥ policy.MIN_DISTANCE
3. AND pattern in {on_site, hybrid}
4. AND policy.MOBILITY_RULE_ACTIVE = TRUE
5. AND NOT contract_type = "probationary"
→ ELIGIBLE — see policy MOB-2025-03
→ REASON: All criteria satisfied
→ LOG: [2025-03-15 10:30] Decision recorded
→ FAIRNESS_CHECK: Automated bias detection passed
→ AUDIT_ID: AUDIT-2025-03-15-001
Trust & context
Designed for public-sector oversight and accountability.
AdminLab.ai combines administrative science, legal reasoning and AI engineering into
a framework prioritizing explainability, auditability, and institutional trust.
Designed in line with emerging AI governance principles, including those reflected in the EU AI Act.
Recogintion in public-administration practice
Public-administration work nominated for the European Ombudsman Award for Good Administration 2025-2026. Our methodology is grounded in established administrative standards.
Explainability as a priority
Each decision point is logged to endable HR, compliance and oversight bodies to review and challenge the reasoning chain if necessary. Fully transparent, no black boxes.
Ready for institutional deployment
Designed for transparent workflows, internal due diligence and seamless integration with existing administrative processes and legacy systems, supporting institutional deployment.
Contact
Collaborations & due diligence.
For partnerships, research collaborations, due-diligence enquiries, or to discuss how explainable AI could support your institution's HR, policy or compliance functions:
contact@adminlab.ai
Response time
Response within 2 business days for initial enquiries
Hours
Monday–Friday, 9:00–17:00 CET
Flexible hours for institutional partners
Flexible hours for institutional partners
Location & jurisdiction
Based in European Union 🇪🇺
Designed in accordance with GDPR requirements
Designed in accordance with GDPR requirements