Lesson 1Vendor an client contracts fi AI features: data processing agreements, joint controllership, liability allocation, an security requirementsDis section explain how fi structure vendor an client contracts fi AI features, focusing pon data processing agreements, joint controllership, liability allocation, an security clauses weh reflect regulatory an ethical requirements.
Defining controller an processor rolesKey data processing agreement clausesJoint controllership an shared dutiesLiability caps, indemnities, an insuranceSecurity an incident response obligationsAudit, oversight, an termination rightsLesson 2Core data protection regimes an obligations relevant to AI (principles: purpose limitation, data minimization, lawful basis, transparency)Dis section review core data protection regimes relevant to AI, emphasizing principles such as purpose limitation, data minimization, lawful basis, an transparency, an how fi operationalize dem across AI development an deployment.
Purpose limitation in AI training an useData minimization an feature selectionChoosing an documenting lawful basesTransparency an meaningful noticesAccuracy, storage limits, an integrityAccountability an governance structuresLesson 3Data Protection Impact Assessments (DPIAs) / AI Impact Assessments (AIA): structure, key questions, an remediation plansDis section explain how fi design an run DPIAs an AIAs, from scoping an risk identification to stakeholder engagement, documentation, an remediation planning, ensuring AI systems meet legal, ethical, an organizational expectations.
Scoping AI systems an processing activitiesIdentifying stakeholders an affected groupsCataloging risks to rights an freedomsDesigning mitigation an remediation plansDocumenting outcomes an sign-offIntegrating DPIAs into product lifecycleLesson 4Algorithmic fairness an bias: sources a bias, measurement methods, an mitigation techniquesDis section analyze algorithmic bias an fairness in AI, explaining sources a bias, fairness metrics, an mitigation strategies across data, modeling, an deployment, wid attention to legal expectations in strict regulatory environments.
Types an sources a algorithmic biasFairness metrics an trade-offsBias in data collection an labelingModel training an evaluation strategiesMitigation during deployment an monitoringDocumentation a fairness decisionsLesson 5Operational playbooks fi product compliance reviews an cross-functional escalation (Product, Legal, Privacy, Compliance)Dis section provide practical playbooks fi product compliance reviews, defining roles, workflows, an escalation paths among Product, Legal, Privacy, an Compliance teams to manage AI risks an document defensible decisions.
Intake an triage a AI product changesRisk-based review levels an criteriaRoles a Product, Legal, Privacy, ComplianceEscalation paths fi high-risk AI use casesDecision documentation an approval recordsFeedback loops into product roadmapsLesson 6Model risk management fi AI features: documentation (model cards), validation, testing, performance monitoring, an explainabilityDis section cover model risk management fi AI features, including documentation, validation, testing, monitoring, an explainability, aligning model governance wid regulatory expectations an internal risk appetite frameworks.
Model inventory an classificationModel cards an documentation standardsValidation an independent challengePerformance, drift, an stability monitoringExplainability methods an limitationsModel change management an decommissioningLesson 7Ethical frameworks fi AI decisions: stakeholder mapping, proportionality, contestability, human oversight, an redress mechanismsDis section introduce ethical frameworks fi AI decision-making, covering stakeholder mapping, proportionality, contestability, human oversight, an redress, an show how fi embed dese principles into governance processes an product design.
Stakeholder an impact mapping fi AIProportionality an necessity assessmentsDesigning contestability an appeal channelsHuman-in-the-loop an on-the-loop modelsRedress an remedy mechanisms fi harmEmbedding ethics reviews into governanceLesson 8Privacy-preserving design: data minimization, differential privacy, anonymization, pseudonymization, an secure multi-party computation basicsDis section explore privacy-preserving design strategies fi AI, including data minimization, anonymization, pseudonymization, differential privacy, an secure multi-party computation, wid guidance pon use cases an implementation trade-offs.
Data minimization in AI feature designAnonymization an re-identification risksPseudonymization an tokenization methodsDifferential privacy fi analytics an MLSecure multi-party computation basicsSelecting appropriate privacy techniquesLesson 9Technical controls: access control, logging, encryption, retention policies, an secure development lifecycle (SDLC) fi MLDis section detail technical safeguards fi AI systems, including access control, logging, encryption, retention, an secure ML development, showing how engineering choices support regulatory compliance an ethical risk reduction.
Role-based an attribute-based access controlSecurity logging an audit trail designEncryption in transit an at rest fi AI dataData retention an deletion automationSecure coding an code review fi MLSecurity testing an hardening a AI servicesLesson 10Assessing lawful bases an consent limits fi workplace surveillance an employee data processingDis section examine lawful bases an consent limits fi workplace surveillance an employee data, addressing monitoring tools, transparency duties, power imbalances, an safeguards to protect dignity an labor rights.
Common workplace surveillance scenariosAssessing legitimate interest an necessityConsent limits in employment contextsTransparency an worker information dutiesSafeguards fi monitoring technologiesEngaging works councils an unionsLesson 11Regulatory trends in high-regulation jurisdictions an compliance pathways fi novel AI productsDis section survey regulatory trends in high-regulation jurisdictions, outlining emerging AI laws, guidance, an enforcement patterns, an mapping practical compliance pathways fi novel AI products an cross-border operations.
Overview a major AI regulatory regimesSector-specific AI rules an guidanceSupervisory expectations an enforcementRegulatory sandboxes an innovation hubsDesigning risk-based compliance programsCross-border data an AI compliance issuesLesson 12Human rights frameworks applicable to data an AI: UN Guiding Principles, GDPR as a rights-based model, an national human-rights implicationsDis section link human rights law to data an AI governance, explaining di UN Guiding Principles, GDPR’s rights-based approach, an how national human rights duties shape corporate responsibilities fi AI design an deployment.
UN Guiding Principles an corporate dutiesGDPR as a rights-based regulatory modelNational human rights laws affecting AISalient human rights risks in AI useHuman rights due diligence fi AIRemedy an accountability expectations
