Artificial Intelligence and Law Course

Lesson 1 • Key Legal Concepts for AI Contexts

Introduces liability, agency, duty of care, and foreseeability as applied to automated systems. These concepts recur throughout the course.

Lesson 2 • Historical Intersections of Technology and Law

Traces how prior technologies—printing, electricity, internet—reshaped legal doctrine. Reveals recurring patterns that predict how AI will be regulated.

Lesson 3 • What AI Systems Actually Do

Covers machine learning, neural networks, and decision automation at a conceptual level. Grounds legal analysis in accurate technical understanding rather than speculation.

Lesson 1 • Privacy by Design in AI Development

Translates privacy law obligations into engineering and product decisions. Equips students to advise development teams on compliant AI architecture.

Lesson 2 • Individual Rights and Automated Decisions

Covers rights to access, correction, erasure, and objection to automated decision-making. Connects individual rights to AI system design requirements.

Lesson 3 • Cross-Border Data Flows and AI

Addresses legal mechanisms governing international data transfers used in global AI deployments. Highlights compliance gaps in multinational AI projects.

Lesson 4 • Personal Data in AI Pipelines

Defines personal data, sensitive categories, and how AI systems collect and process them. Links data types to differential legal treatment.

Lesson 1 • Patent Law and AI-Assisted Invention

Covers inventorship doctrine, patentability of AI outputs, and disclosure requirements. Addresses the growing tension between AI autonomy and human-inventor rules.

Lesson 2 • Copyright in AI Training and Output

Analyses whether training on copyrighted data constitutes infringement and who owns AI-generated works. Directly shapes content and media AI product strategy.

Lesson 3 • Trade Secrets and Model Protection

Examines how trade secret law protects AI models, weights, and training datasets. Identifies misappropriation risks in employment and vendor relationships.

Lesson 1 • Product Liability Applied to AI Systems

Maps manufacturing defect, design defect, and failure-to-warn theories onto AI products. Establishes the primary liability framework for AI harm claims.

Lesson 2 • Emerging Strict Liability Proposals for AI

Reviews legislative and academic proposals for strict liability regimes targeting high-risk AI. Prepares students for a shifting liability landscape.

Lesson 3 • Autonomous Systems and Novel Liability Gaps

Addresses liability vacuums created by fully autonomous AI agents acting without human oversight. Explores doctrinal adaptations and legislative responses.

Lesson 4 • Negligence and Duty of Care for AI

Applies the negligence elements—duty, breach, causation, damages—to AI deployment decisions. Builds analytical skill for advising clients on risk mitigation.

Lesson 1 • Risk-Based Regulatory Architecture

Explains how regulators classify AI by risk level and impose proportionate obligations. Provides the structural logic underlying most current AI regulatory frameworks.

Lesson 2 • Transparency and Explainability Requirements

Covers mandatory disclosure, labelling, and explainability obligations imposed on AI systems. Links technical interpretability methods to legal compliance needs.

Lesson 3 • Sector-Specific AI Regulation

Surveys AI rules in financial services, healthcare, employment, and critical infrastructure. Shows how horizontal AI law interacts with vertical sector regulation.

Lesson 4 • Building an AI Compliance Programme

Translates regulatory requirements into organisational policies, controls, and governance structures. Equips students to design and audit AI compliance programmes.

Lesson 1 • High-Stakes AI Discrimination Domains

Applies discrimination analysis to hiring, lending, housing, and criminal justice AI. Illustrates how context shapes legal standards and remediation obligations.

Lesson 2 • Auditing AI Systems for Bias

Covers technical and legal audit methodologies for detecting and documenting algorithmic bias. Prepares students to commission, interpret, and act on AI audits.

Lesson 3 • Discrimination Law Fundamentals

Reviews protected characteristics, disparate treatment, and disparate impact theories. Establishes the doctrinal baseline for analysing AI discrimination claims.

Lesson 1 • Professional Responsibility and Competence

Applies attorney competence, supervision, and confidentiality duties to AI tool use. Identifies disciplinary risks and best practices for responsible AI adoption.

Lesson 2 • AI in Judicial Decision-Making

Analyses risk assessment tools, sentencing algorithms, and case prediction systems used by courts. Raises due process and equal protection concerns for judicial AI.

Lesson 3 • AI Evidence and Admissibility

Examines authentication, hearsay, and expert witness rules as applied to AI-generated evidence. Prepares students to challenge or defend AI evidence in litigation.

Lesson 4 • AI Tools in Legal Research and Drafting

Surveys generative AI, legal research platforms, and contract automation tools. Grounds professional responsibility analysis in concrete tool capabilities.

Lesson 1 • AI Ethics Frameworks and Legal Relevance

Maps ethical principles—fairness, accountability, transparency, safety—to legal obligations and reputational risk. Shows how ethics frameworks anticipate future regulation.

Lesson 2 • Contracting for AI Products and Services

Drafts and negotiates AI-specific contract provisions covering performance, liability, data rights, and audit. Directly applicable to vendor, partnership, and procurement work.

Lesson 3 • Strategic Legal Counsel on AI Initiatives

Integrates regulatory, liability, IP, and governance analysis into strategic advice for AI product launches and M&A. Culminates the course with applied counselling practice.

Lesson 4 • Enterprise AI Governance Structures

Designs board-level oversight, AI review committees, and accountability mechanisms for AI deployment. Equips students to build governance infrastructure in organisations.