Lesson 1DPIA fi AI systems: scopin model inputs, outputs, risk scorin, error rates an mitigation strategiesDis section walk thru DPIAs fi AI HR tools, coverin scope definition, mappin inputs an outputs, risk scorin, assessin error rates an bias, an designin mitigation an monitorin plans aligned wid GDPR an 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 an governance: model risk register, algorithmic impact statement, change logs an trainin recordsDis section explain how fi document AI HR tools thru model risk registers, impact statements, change logs, an trainin records, enablin traceability, accountability, an defensible evidence fi regulators, courts, an 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 a GDPR to AI: lawful basis fi processing, special categories, an implications fi automated decision-makin (Art. 22)Dis section clarify how GDPR apply to AI in HR, includin lawful bases, handlin special category data, profilin, an automated decisions under Article 22, an how fi design governance, records, an safeguards dat 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 an ethical risks when usin AI fi applicant screenin an employee monitorinDis section analyze legal an ethical risks a AI in hirin an monitorin, includin discrimination, chillin effects, excessive surveillance, an misuse a inferred data, an show how fi embed safeguards, oversight, an 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 an non-discrimination checks: dataset provenance, representativeness, explainability an third-party auditsDis section cover bias an fairness controls fi AI HR tools, includin dataset provenance, representativeness checks, explainability techniques, fairness metrics, an independent audits, wid guidance pon remediation an communication a 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 an secure model deploymentDis section detail technical safeguards fi AI in HR, includin data minimization, anonymization an pseudonymization, access controls, an secure deployment patterns, ensurin confidentiality, integrity, an resilience a models an HR data over dem 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 an transparency: notice, meaningful explanation a automated decisions, human review an opt-out optionsDis section explain employee information rights in AI-driven HR, includin layered notices, meaningful explanations a logic, human review options, contestin decisions, an practical opt-out or alternative procedures consistent wid GDPR an 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 an co-determination requirements in Germany: participation, information rights an consultation obligationsDis section focus pon German works council co-determination fi AI HR tools, coverin participation triggers, information rights, consultation duties, typical Betriebsvereinbarungen clauses, an strategies fi early, trust-based engagement wid employee representatives.
When AI tools trigger co-determinationInformation and inspection rights of councilsStructuring consultation and negotiationsKey clauses in AI BetriebsvereinbarungenCooperation strategies and documentationLesson 9Testin an validation procedures: pre-deployment testin, performance metrics, monitorin, an periodic re-evaluationDis section set out testin an validation practices fi AI HR systems, includin pre-deployment checks, performance an fairness metrics, monitorin in production, periodic re-evaluation, rollback plans, an documentin results fi regulators an 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 an vendor management: processor vs controller roles, required contract clauses, SLAs, model change management an model provenance requestsDis section address contracts an vendor oversight fi AI HR tools, definin controller an processor roles, mandatory GDPR clauses, SLAs, security an audit rights, model change notifications, an provenance an documentation obligations fi suppliers.
Allocating controller and processor rolesGDPR Article 28 and DPA essentialsSecurity, uptime and support SLAsModel updates, drift and change controlProvenance, audit and termination rights
