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Good practice Imported

MIRA — Albania's AI Risk-Assessment System for Labour Inspection Targeting

Albania · Tirana · See the Albania profile · See the Tirana profile

Evidence: Descriptive / self-reported Top 25% 73/100 · Ask Evidence Copilot about this practice

Albania's labour inspectorate replaced its manual 'Matrix of Penalties' with MIRA, a nine-algorithm ML risk tool that now drives 70% of its 12,000+ annual inspections, showing a 30% gain in predicting undeclared work, per ILO/EU reporting and independent Albanian press.

12,000+
Annual labour inspections
70%
Inspections planned using MIRA risk assessment
30%
Inspections kept random (validation)
30%
Improvement in predicting undeclared/underdeclared work vs prior method
~68%
Confirmed informal-employment cases from MIRA-planned inspections
100+
Variables used per inspection risk score
9
Machine-learning algorithms in MIRA

Details

Maturity
Established
Promoter
State Inspectorate for Labour and Social Services (ISHPSH), Albania — with ILO technical support and EU funding
Period
2023-present
Keywords
labour inspection, compliance, risk assessment, machine learning

Context

Informal employment has affected more than half of Albania's workforce in recent years, concentrated in agriculture, trade, construction and hospitality. Albania's State Inspectorate for Labour and Social Services (ISHPSH) previously relied on a static rules-based 'Matrix of Penalties' to decide which businesses to inspect. With technical support from the International Labour Organization (ILO) and funding from the European Union, ISHPSH built MIRA (Matrix of Intelligence and Risk Assessment), which runs nine machine-learning algorithms over historical inspection data — business size, sector, location, seasonal patterns, working hours and compliance history across 100+ variables per inspection — to score which employers are most likely to be non-compliant. MIRA went live in November 2023, replacing the previous rules-based system.

Results

With more than 12,000 inspections conducted annually, 70% are now planned using MIRA's risk assessment (the remaining 30% stay random, to check the model isn't missing blind spots). ISHPSH and the ILO report a 30% improvement in predicting undeclared and underdeclared work compared to the previous manual method, and about 68% of confirmed informal-employment cases now come from MIRA-planned inspections rather than random ones. No independent third-party audit of MIRA's fairness or error rates has been published.

Implementation

Indicative cost
Medium (€50k–€500k)
Time to results
Medium (1–3 years)
Staffing & skills
State Inspectorate for Labour and Social Services (ISHPSH) inspectors, ILO technical support team

Conditions for success

  • keeping a 30% random-inspection share to check the model isn't missing blind spots
  • integrating case management with the risk-scoring engine
  • sufficient quality historical inspection data across 100+ variables

Common failure modes

  • no independent third-party audit of fairness or error rates published
  • European Labour Authority has flagged bias/explainability as an open issue for AI-driven inspection risk-scoring generally

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Data sources

Where this practice's information was retrieved from, and when.

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