Artificial Intelligence and Law Training Course

Participants can apply core AI Act duties to proposed AI use cases.

• EU AI Act risk categories and prohibited practices examined
• High-risk system requirements for risk management, data governance and documentation
• Transparency duties for chatbots, synthetic content and emotion recognition systems
• General-purpose AI obligations, model documentation and downstream information duties
• Conformity assessment, post-market monitoring and serious incident reporting requirements
• Extraterritorial reach assessed through providers, deployers and affected persons

Participants can assess personal data processing and automated decision risks in AI systems.

• GDPR roles, lawful bases and purpose limitation applied to AI
• Article 22 safeguards assessed for solely automated significant decisions
• Data protection impact assessments structured around necessity, proportionality and residual risk
• Special category data controls for training, testing and model operation
• Data minimisation techniques including sampling, masking, pseudonymisation and retention limits
• Cross-border transfers evaluated for cloud-hosted models and vendor support

Participants can identify intellectual property and confidentiality issues affecting AI inputs and outputs.

• Copyright subsistence assessed in training data, prompts and generated outputs
• Text and data mining exceptions compared with licence restrictions
• Trade secret safeguards applied to proprietary datasets and confidential prompts
• Database rights and scraping restrictions examined for data acquisition
• Output ownership clauses reviewed for allocation, warranties and permitted use
• Attribution, provenance and content labelling requirements considered for synthetic media

Participants can identify likely liability routes and allocate responsibilities for AI-related harm.

• Negligence analysis connects foreseeability, duty, causation and loss
• Product liability principles applied to defective AI-enabled products and services
• Misrepresentation risks arising from inaccurate automated advice and content
• Consumer protection duties tested against dark patterns and misleading claims
• Causation evidence mapped across models, data, users and operational controls
• Incident records structured to preserve evidence for complaints and claims

Participants can assess discrimination, employment and rights-based concerns in AI decisions.

• Indirect discrimination tested through disparate impact and selection-rate analysis
• Employment AI uses assessed for recruitment, performance and disciplinary decisions
• Human rights principles applied to privacy, equality, dignity and due process
• Meaningful human review defined through authority, competence and intervention records
• Explainability requirements matched to decision significance, audience and contestability
• Accessibility and reasonable adjustment considerations incorporated into AI service design

Participants can review AI procurement arrangements for legal protections and operational accountability.

• AI supplier due diligence covers data sources, model controls and incidents
• Statements of work define intended use, performance measures and exclusions
• Data processing clauses allocate controller, processor and subprocessor responsibilities
• Audit rights specify evidence, frequency, remediation and access limitations
• Indemnities address intellectual property claims, data breaches and regulatory penalties
• Exit provisions cover data return, model transition and business continuity

Participants can design governance controls for accountable AI approval and monitoring.

• NIST AI RMF functions applied to govern, map, measure and manage
• ISO/IEC 42001 management system elements adapted for AI governance
• Three lines model assigns business, risk and assurance responsibilities
• AI use-case inventory records purpose, data, owner and risk rating
• Approval gates establish legal review, testing evidence and accountable sign-off
• Monitoring metrics track drift, errors, complaints, overrides and incidents

Participants can set practical legal controls for generative AI tools and outputs.

• Prompt handling rules protect confidential, personal and privileged information
• Retrieval-augmented generation risks assessed for source quality and access control
• Hallucination controls combine grounding, review thresholds and citation requirements
• Synthetic content policies address disclosure, impersonation and harmful outputs
• Acceptable use rules distinguish approved tools, users and prohibited activities
• Output review protocols assign validation duties before external publication

Participants can communicate AI legal advice and plan proportionate implementation actions.

• Legal advice memoranda separate facts, assumptions, risks and recommended controls
• Board briefings present risk appetite, decisions, residual exposure and accountability
• AI incident response integrates legal privilege, notification and evidence preservation
• Regulator engagement plans define records, spokespersons and response timetables
• Escalation criteria identify prohibited uses, high-risk decisions and serious incidents
• Ninety-day action plans prioritise policies, contracts, training and assurance