Aralin 1Vendor at client contracts para sa AI features: data processing agreements, joint controllership, liability allocation, at security requirementsIpinaliliwanag ng seksyong ito kung paano i-structure ang vendor at client contracts para sa AI features, nakatuon sa data processing agreements, joint controllership, liability allocation, at security clauses na sumasalamin sa regulatory at ethical requirements.
Pagdefine ng controller at processor rolesKey data processing agreement clausesJoint controllership at shared dutiesLiability caps, indemnities, at insuranceSecurity at incident response obligationsAudit, oversight, at termination rightsAralin 2Core data protection regimes at obligations relevant sa AI (principles: purpose limitation, data minimization, lawful basis, transparency)Sinusuri ng seksyong ito ang core data protection regimes na relevant sa AI, binibigyang-diin ang principles tulad ng purpose limitation, data minimization, lawful basis, at transparency, at kung paano i-operationalize ang mga ito sa buong AI development at deployment.
Purpose limitation sa AI training at useData minimization at feature selectionPagpili at pagdokumenta ng lawful basesTransparency at meaningful noticesAccuracy, storage limits, at integrityAccountability at governance structuresAralin 3Data Protection Impact Assessments (DPIAs) / AI Impact Assessments (AIA): structure, key questions, at remediation plansIpinaliliwanag ng seksyong ito kung paano magdidisenyo at mag-run ng DPIAs at AIAs, mula scoping at risk identification hanggang stakeholder engagement, dokumentasyon, at remediation planning, tinitiyak na ang AI systems ay sumusunod sa legal, ethical, at organizational expectations.
Scoping ng AI systems at processing activitiesPagkilala ng stakeholders at affected groupsPagkatala ng risks sa rights at freedomsPagdidisenyo ng mitigation at remediation plansPagdokumenta ng outcomes at sign-offPag-integrate ng DPIAs sa product lifecycleAralin 4Algorithmic fairness at bias: sources ng bias, measurement methods, at mitigation techniquesAnalisis ng algorithmic bias at fairness sa AI ng seksyong ito, ipinaliliwanag ang sources ng bias, fairness metrics, at mitigation strategies sa data, modeling, at deployment, na may pansin sa legal expectations sa strict regulatory environments.
Types at sources ng algorithmic biasFairness metrics at trade-offsBias sa data collection at labelingModel training at evaluation strategiesMitigation sa deployment at monitoringPagdokumenta ng fairness decisionsAralin 5Operational playbooks para sa product compliance reviews at cross-functional escalation (Product, Legal, Privacy, Compliance)Nagbibigay ng praktikal na playbooks ang seksyong ito para sa product compliance reviews, nagdedefine ng roles, workflows, at escalation paths sa pagitan ng Product, Legal, Privacy, at Compliance teams upang pamahalaan ang AI risks at magdokumenta ng defensible decisions.
Intake at triage ng AI product changesRisk-based review levels at criteriaRoles ng Product, Legal, Privacy, ComplianceEscalation paths para sa high-risk AI use casesDecision documentation at approval recordsFeedback loops sa product roadmapsAralin 6Model risk management para sa AI features: documentation (model cards), validation, testing, performance monitoring, at explainabilityTinutukan ng seksyong ito ang model risk management para sa AI features, kabilang ang documentation, validation, testing, monitoring, at explainability, na nag-aayon ng model governance sa regulatory expectations at internal risk appetite frameworks.
Model inventory at classificationModel cards at documentation standardsValidation at independent challengePerformance, drift, at stability monitoringExplainability methods at limitationsModel change management at decommissioningAralin 7Ethical frameworks para sa AI decisions: stakeholder mapping, proportionality, contestability, human oversight, at redress mechanismsIpinapakilala ng seksyong ito ang ethical frameworks para sa AI decision-making, tinututukan ang stakeholder mapping, proportionality, contestability, human oversight, at redress, at nagpapakita kung paano i-embed ang mga prinsipyong ito sa governance processes at product design.
Stakeholder at impact mapping para sa AIProportionality at necessity assessmentsPagdidisenyo ng contestability at appeal channelsHuman-in-the-loop at on-the-loop modelsRedress at remedy mechanisms para sa harmPag-embed ng ethics reviews sa governanceAralin 8Privacy-preserving design: data minimization, differential privacy, anonymization, pseudonymization, at secure multi-party computation basicsNag-eeksplora ng privacy-preserving design strategies para sa AI ang seksyong ito, kabilang ang data minimization, anonymization, pseudonymization, differential privacy, at secure multi-party computation, na may gabay sa use cases at implementation trade-offs.
Data minimization sa AI feature designAnonymization at re-identification risksPseudonymization at tokenization methodsDifferential privacy para sa analytics at MLSecure multi-party computation basicsPagpili ng appropriate privacy techniquesAralin 9Technical controls: access control, logging, encryption, retention policies, at secure development lifecycle (SDLC) para sa MLNagdedetalye ng technical safeguards para sa AI systems ang seksyong ito, kabilang ang access control, logging, encryption, retention, at secure ML development, na nagpapakita kung paano sumusuporta ang engineering choices sa regulatory compliance at ethical risk reduction.
Role-based at attribute-based access controlSecurity logging at audit trail designEncryption in transit at at rest para sa AI dataData retention at deletion automationSecure coding at code review para sa MLSecurity testing at hardening ng AI servicesAralin 10Assessing lawful bases at consent limits para sa workplace surveillance at employee data processingSinusuri ng seksyong ito ang lawful bases at consent limits para sa workplace surveillance at employee data, tinututukan ang monitoring tools, transparency duties, power imbalances, at safeguards upang protektahan ang dignity at labor rights.
Common workplace surveillance scenariosAssessing legitimate interest at necessityConsent limits sa employment contextsTransparency at worker information dutiesSafeguards para sa monitoring technologiesPag-engage ng works councils at unionsAralin 11Regulatory trends sa high-regulation jurisdictions at compliance pathways para sa novel AI productsNagbibigay ng survey ng regulatory trends sa high-regulation jurisdictions ang seksyong ito, nag-o-outline ng emerging AI laws, guidance, at enforcement patterns, at nagma-map ng praktikal na compliance pathways para sa novel AI products at cross-border operations.
Overview ng major AI regulatory regimesSector-specific AI rules at guidanceSupervisory expectations at enforcementRegulatory sandboxes at innovation hubsPagdidisenyo ng risk-based compliance programsCross-border data at AI compliance issuesAralin 12Human rights frameworks applicable sa data at AI: UN Guiding Principles, GDPR as a rights-based model, at national human-rights implicationsNag-uugnay ng human rights law sa data at AI governance ang seksyong ito, ipinaliliwanag ang UN Guiding Principles, rights-based approach ng GDPR, at kung paano hinuhubog ng national human rights duties ang corporate responsibilities para sa AI design at deployment.
UN Guiding Principles at corporate dutiesGDPR as a rights-based regulatory modelNational human rights laws affecting AISalient human rights risks sa AI useHuman rights due diligence para sa AIRemedy at accountability expectations
