Lesson 1DPIA for AI systems: scoping model inputs, outputs, risk scoring, error rates and mitigation strategiesThis section walks through DPIAs for AI HR tools, covering scope definition, mapping inputs and outputs, risk scoring, assessing error rates and bias, and designing mitigation and monitoring plans aligned with GDPR and labor law expectations.
Scoping AI use cases and data flowsIdentifying data subjects and impactsRisk scoring and prioritization methodsEvaluating error rates and false matchesMitigation, residual risk and sign-offLesson 2Documentation and governance: model risk register, algorithmic impact statement, change logs and training recordsThis section explains how to document AI HR tools through model risk registers, impact statements, change logs, and training records, enabling traceability, accountability, and defensible evidence for regulators, courts, and employee representatives.
Designing an AI model risk registerAlgorithmic impact statement structureMaintaining model and data change logsTracking training data and model versionsEvidence packs for audits and litigationLesson 3Applicability of GDPR to AI: lawful basis for processing, special categories, and implications for automated decision-making (Art. 22)This section clarifies how GDPR applies to AI in HR, including lawful bases, handling special category data, profiling, and automated decisions under Article 22, and how to design governance, records, and safeguards that withstand regulatory scrutiny.
Choosing lawful bases for HR AI usesHandling special category and union dataProfiling and automated decision criteriaMeaningful human involvement safeguardsRopa and documentation for AI systemsLesson 4Legal and ethical risks when using AI for applicant screening and employee monitoringThis section analyzes legal and ethical risks of AI in hiring and monitoring, including discrimination, chilling effects, excessive surveillance, and misuse of inferred data, and shows how to embed safeguards, oversight, and proportionality into HR AI deployments.
Discrimination and equal treatment risksSurveillance, trust and chilling effectsOver-collection and function creep in HRUse of inferred and behavioral dataEthics review and escalation channelsLesson 5Bias, fairness and non-discrimination checks: dataset provenance, representativeness, explainability and third-party auditsThis section covers bias and fairness controls for AI HR tools, including dataset provenance, representativeness checks, explainability techniques, fairness metrics, and independent audits, with guidance on remediation and communication of residual risks.
Tracing dataset sources and licensesAssessing representativeness and coverageFairness metrics and threshold settingExplainability tools for HR decisionsThird-party audits and remediation plansLesson 6Technical measures: data minimization, anonymization/pseudonymization, access controls and secure model deploymentThis section details technical safeguards for AI in HR, including data minimization, anonymization and pseudonymization, access controls, and secure deployment patterns, ensuring confidentiality, integrity, and resilience of models and HR data over their lifecycle.
Data minimization for HR training datasetsAnonymization and pseudonymization patternsRole-based and attribute-based access controlSecure model hosting and API hardeningKey management and logging for AI systemsLesson 7Employee rights and transparency: notice, meaningful explanation of automated decisions, human review and opt-out optionsThis section explains employee information rights in AI-driven HR, including layered notices, meaningful explanations of logic, human review options, contesting decisions, and practical opt-out or alternative procedures consistent with GDPR and labor law.
Designing clear AI use notices for staffExplaining model logic in plain languageSetting up human review and escalationHandling objections and contestationsDocumenting responses to rights requestsLesson 8Works council and co-determination requirements in Germany: participation, information rights and consultation obligationsThis section focuses on German works council co-determination for AI HR tools, covering participation triggers, information rights, consultation duties, typical Betriebsvereinbarungen clauses, and strategies for early, trust-based engagement with employee representatives.
When AI tools trigger co-determinationInformation and inspection rights of councilsStructuring consultation and negotiationsKey clauses in AI BetriebsvereinbarungenCooperation strategies and documentationLesson 9Testing and validation procedures: pre-deployment testing, performance metrics, monitoring, and periodic re-evaluationThis section sets out testing and validation practices for AI HR systems, including pre-deployment checks, performance and fairness metrics, monitoring in production, periodic re-evaluation, rollback plans, and documenting results for regulators and works councils.
Pre-deployment functional test plansPerformance, error and fairness metricsShadow mode and A/B testing in HROngoing monitoring and alert thresholdsPeriodic reviews and rollback criteriaLesson 10Contractual and vendor management: processor vs controller roles, required contract clauses, SLAs, model change management and model provenance requestsThis section addresses contracts and vendor oversight for AI HR tools, defining controller and processor roles, mandatory GDPR clauses, SLAs, security and audit rights, model change notifications, and provenance and documentation obligations for suppliers.
Allocating controller and processor rolesGDPR Article 28 and DPA essentialsSecurity, uptime and support SLAsModel updates, drift and change controlProvenance, audit and termination rights
