The system influences employment conditions, promotion, termination or contractual relationship.
Annex III point 4b
AI in worker management, monitoring and performance evaluation
Route 4b concerns AI systems that influence decisions within employment relationships: employment conditions, promotion, termination, task allocation, monitoring and performance or behaviour evaluation.
This page makes the worker management route within Annex III point 4 concrete. The key question is whether AI output can influence conditions, opportunities, evaluation, work distribution or behaviour and performance conclusions for employees or self-employed persons.
Updated: May 202613 min read
When does a use case point to 4b?
Focus on consequences for existing employment relationships. A reporting dashboard differs from a system that evaluates performance, allocates tasks or steers managers towards promotion, termination or disciplinary steps.
The system allocates tasks based on behaviour, traits or personal characteristics.
The system monitors or evaluates performance or behaviour of persons.
The output is used by managers, HR or operations in decisions about people.
Examples of worker-management AI in route 4b
The examples below help define the route. Monitoring and task allocation in particular require careful context analysis.
| Use case | Why sensitive | Evidence |
|---|---|---|
| Performance and behaviour scores | May influence evaluation, compensation, improvement plans or disciplinary steps. | Criteria, data points, explainability, human assessment, objection route and logging. |
| Task allocation and scheduling | May affect workload, opportunities, income or access to attractive assignments. | Purpose, characteristics used, proportionality, exception route and human review. |
| Monitoring and productivity analysis | Affects privacy, autonomy, trust and possible employment-law consequences. | Data minimisation, scope, worker representation, transparency and escalation procedure. |
| Promotion, attrition or termination | May affect career, contractual relationship and position of workers or self-employed persons. | Decision influence, validation, bias analysis, human reassessment and file log. |
| Workforce analytics dashboards | May remain outside scope for aggregated planning, but can move towards 4b when used for individual scoring or prediction. | Aggregation level, use limitation, access, audit trail and prohibition on individual automated conclusions. |
Evidence route 4b needs
For worker management, the file should show what consequences AI output can have for employees or self-employed persons and how human review, privacy and proportionality are safeguarded.
Evidence item 1
Use-case register with purpose, owner, affected groups, data flows and decision impact.
Evidence item 2
AI Act classification note with route 4b, intended use and borderline cases.
Evidence item 3
GDPR, employment-law and bias check around monitoring, data minimisation, retention and special risks.
Evidence item 4
Human oversight instruction for line manager, HR, planning or operations, including objection and escalation.
Evidence item 5
Transparency texts for workers or self-employed persons about purpose, data use, consequences and human review.
Evidence item 6
Training records for HR, managers, operations, legal, compliance and dashboard users.
Borderline cases in 4b
Not every HR dashboard is route 4b. The question is whether individual persons are evaluated, monitored, steered or affected by decisions.
Aggregated workforce planning
May remain outside 4b when the data does not lead to individual conclusions or decisions. Record aggregation, access and use limitations.
Productivity reporting
Becomes more sensitive when reporting leads to individual evaluation, coaching, warning, task allocation or contract decisions.
Attrition or absence prediction
Pay attention when predictions about individuals are used for interventions, opportunities, supervision, promotion or termination.
From worker-management use case to evidence file
Use these steps to move from scattered tool information to a demonstrable file.
Step 1
Inventory AI functions in HRIS, planning, performance, monitoring, operations and workforce analytics.
Step 2
Determine whether the function influences individual employment decisions, task allocation, monitoring or performance evaluation.
Step 3
Record GDPR, employment-law context, proportionality, worker representation and data minimisation.
Step 4
Describe where HR, managers or operations review, deviate and escalate.
Step 5
Align transparency and role-based AI literacy with the people who use the output or are affected by it.
Annex III point 4b
Use route 4b as a working layer inside the HR-AI hub
Start with the whitepaper for context, then use the Evidence Pack to record monitoring, task allocation and performance evaluation as concrete use cases.
Sources and related routes
Use this route together with the broad hub, the classification check and the template pack.
Annex III point 4 hub
Use the broad hub to assess route 4a and 4b together.
Open routeAnnex III point 4 classification check
Get an initial indication whether your use case points to 4a, 4b or outside scope.
Open routeHR-AI Evidence & Readiness
Read the whitepaper as the explanation layer above the template pack.
Open routeHR-AI Evidence Pack
Download editable Word and Excel templates for route 4a and 4b.
Open routeAI in recruitment and selection under the AI Act
Compare this route with the other Annex III point 4 route.
Open routeNext route
Next steps outside Responsible AI Platform
Responsible AI Platform remains the source layer. For implementation or training, connect this route to the right environment.
Embed AI
Embed AI for worker-management AI evidence
For classification, gap analysis and evidence around task allocation, monitoring, performance, promotion and termination.
LearnWize
LearnWize for role-based training
For AI literacy and training records for HR, managers and operations teams.
FAQ about route 4b
Does every HR dashboard fall under Annex III point 4b?
No. An aggregated planning dashboard differs from a system that monitors, scores, allocates tasks to or supports evaluations of individual workers. Look at individual impact.
Why is monitoring especially sensitive?
Monitoring affects privacy, autonomy and employment relationships. When AI output can lead to evaluation, coaching, warning or other consequences, document the route carefully.
What does human oversight mean for worker-management AI?
The responsible human must understand the output, add context, deviate, handle objections and record why AI output was or was not followed.
Should worker representation be involved?
That depends on the organisation, system and deployment. For monitoring, workforce tracking or significant changes to work processes, worker representation is often an important point of attention.
Sources
This page uses the AI Act, the draft high-risk classification guidelines and relevant supervisory context as its basis.