Aralin 1DPIA para sa AI systems: scoping ng model inputs, outputs, risk scoring, error rates at mitigation strategiesNaglalakad ang seksyong ito sa DPIAs para sa AI HR tools, na sumasaklaw sa scope definition, pagmama-map ng inputs at outputs, risk scoring, pag-assess ng error rates at bias, at pagdidisenyo ng mitigation at monitoring plans na nakahanay sa GDPR at 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-offAralin 2Documentation at governance: model risk register, algorithmic impact statement, change logs at training recordsIpinaliliwanag ng seksyong ito kung paano idokumento ang AI HR tools sa pamamagitan ng model risk registers, impact statements, change logs, at training records, na nagbibigay-daan sa traceability, accountability, at defensible evidence para sa regulators, korte, at 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 litigationAralin 3Applicability ng GDPR sa AI: lawful basis para sa processing, special categories, at implications para sa automated decision-making (Art. 22)Lilinawin ng seksyong ito kung paano naaaplay ang GDPR sa AI sa HR, kabilang ang lawful bases, paghawak ng special category data, profiling, at automated decisions sa ilalim ng Article 22, at kung paano magdidisenyo ng governance, records, at safeguards na matatag sa 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 systemsAralin 4Legal at ethical risks sa paggamit ng AI para sa applicant screening at employee monitoringSinusuri ng seksyong ito ang legal at ethical risks ng AI sa hiring at monitoring, kabilang ang discrimination, chilling effects, excessive surveillance, at misuse ng inferred data, at nagpapakita kung paano i-embed ang safeguards, oversight, at proportionality sa 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 channelsAralin 5Bias, fairness at non-discrimination checks: dataset provenance, representativeness, explainability at third-party auditsTinutukoy ng seksyong ito ang bias at fairness controls para sa AI HR tools, kabilang ang dataset provenance, representativeness checks, explainability techniques, fairness metrics, at independent audits, na may gabay sa remediation at komunikasyon ng residual risks.
Tracing dataset sources and licensesAssessing representativeness and coverageFairness metrics and threshold settingExplainability tools for HR decisionsThird-party audits and remediation plansAralin 6Technical measures: data minimization, anonymization/pseudonymization, access controls at secure model deploymentTinalakay ng seksyong ito ang technical safeguards para sa AI sa HR, kabilang ang data minimization, anonymization at pseudonymization, access controls, at secure deployment patterns, na nagsisiguro ng confidentiality, integrity, at resilience ng models at HR data sa buong 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 systemsAralin 7Mga karapatan ng employee at transparency: notice, meaningful explanation ng automated decisions, human review at opt-out optionsIpinaliliwanag ng seksyong ito ang mga information rights ng employee sa AI-driven HR, kabilang ang layered notices, meaningful explanations ng logic, human review options, contesting decisions, at practical opt-out o alternative procedures na consistent sa GDPR at 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 requestsAralin 8Works council at co-determination requirements sa Alemanya: participation, information rights at consultation obligationsNakatuon ang seksyong ito sa Alemanyang works council co-determination para sa AI HR tools, na sumasaklaw sa participation triggers, information rights, consultation duties, typical Betriebsvereinbarungen clauses, at estratehiya para sa maagang, trust-based engagement sa employee representatives.
When AI tools trigger co-determinationInformation and inspection rights of councilsStructuring consultation and negotiationsKey clauses in AI BetriebsvereinbarungenCooperation strategies and documentationAralin 9Testing at validation procedures: pre-deployment testing, performance metrics, monitoring, at periodic re-evaluationInilalahad ng seksyong ito ang testing at validation practices para sa AI HR systems, kabilang ang pre-deployment checks, performance at fairness metrics, monitoring sa production, periodic re-evaluation, rollback plans, at pagdokumento ng results para sa regulators at 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 criteriaAralin 10Contractual at vendor management: processor vs controller roles, required contract clauses, SLAs, model change management at model provenance requestsTinutugunan ng seksyong ito ang kontrata at vendor oversight para sa AI HR tools, na naglilimita ng controller at processor roles, mandatory GDPR clauses, SLAs, security at audit rights, model change notifications, at provenance at documentation obligations para sa suppliers.
Allocating controller and processor rolesGDPR Article 28 and DPA essentialsSecurity, uptime and support SLAsModel updates, drift and change controlProvenance, audit and termination rights
