Since ~2018, Flanders' employment service VDAB has used a Random Forest ML model, "Next-Step", to predict jobseekers' re-employment odds and route those at highest long-term unemployment risk to intensive caseworker support, screening new registrants within six weeks.
Details
Maturity
Established
Promoter
VDAB (Vlaamse Dienst voor Arbeidsbemiddeling en Beroepsopleiding) — Flemish Public Employment Service
Period
2018–present
Keywords
public employment services, algorithmic profiling, labour-market forecasting, explainable AI, jobseeker support
Context
Since approximately 2018, VDAB (Vlaamse Dienst voor Arbeidsbemiddeling en Beroepsopleiding), the public employment service for Belgium's Flanders region, has used a machine-learning profiling tool internally called 'Next-Step' to estimate each new jobseeker's probability of finding work within a given time horizon.
Objectives
The tool aims to classify all new jobseekers within six weeks of registration, using predicted re-employment probability to prioritise caseworker time toward those assessed as least likely to find work unaided, rather than spreading support evenly across all registrants.
Activities
The underlying model is a Random Forest trained on jobseekers' registration and labour-market history data. VDAB has presented the model's design at EU-level forums such as Cedefop, and peer-reviewed research has applied explainable-AI techniques such as TreeSHAP to VDAB-linked profiling data to assess interpretability and fairness.
Conclusions
Unlike comparable profiling systems in Poland (ruled unconstitutional and discontinued in 2019) and Austria (blocked by the data-protection authority before nationwide rollout), Next-Step has remained in continuous operational use, but no independently audited accuracy or misclassification figures specific to it have been published.
Implementation
Indicative cost
Medium (€50k–€500k) — In-house Random Forest profiling model integrated into existing VDAB registration workflow; no public cost figures found in source.
Time to results
Long (> 3 years) — Operational continuously since approximately 2018 to present.
Staffing & skills
VDAB caseworkers applying model outputs to prioritise support, VDAB data/technical team maintaining the Random Forest model
Conditions for success
Screening embedded into standard registration workflow (classification within six weeks)
Continuous operation since 2018, avoiding the legal/constitutional challenges faced by comparable Polish and Austrian systems
Openness to external academic scrutiny (TreeSHAP-based fairness/interpretability research) and public presentation of methodology at EU forums (Cedefop)
Common failure modes
No independently audited accuracy or misclassification-rate figures specific to Next-Step have been published
Comparable systems elsewhere (Poland, Austria) were discontinued or blocked over legal and fairness concerns
Where it fits
Governance type
regional public employment service
Scale
regional (Flanders, Belgium — all new jobseeker registrants)
Income level
high-income (Belgium)
Data sources
Where this practice's information was retrieved from, and when.