Artificial Intelligence and Law Course

Lesson 1 • Key Legal Concepts for AI Contexts

This 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

This traces how prior technologies—printing, electricity, internet—reshaped legal doctrine. It reveals recurring patterns that predict how AI will be regulated.

Lesson 3 • What AI Systems Actually Do

This covers machine learning, neural networks, and decision automation at a conceptual level. It grounds legal analysis in accurate technical understanding rather than speculation.

Lesson 1 • Privacy by Design in AI Development

This translates privacy law obligations into engineering and product decisions. It equips you to advise development teams on compliant AI architecture.

Lesson 2 • Individual Rights and Automated Decisions

This covers rights to access, correction, erasure, and objection to automated decision-making. It connects individual rights to AI system design requirements.

Lesson 3 • Cross-Border Data Flows and AI

This addresses legal mechanisms governing international data transfers used in global AI deployments. It highlights compliance gaps in multinational AI projects.

Lesson 4 • Personal Data in AI Pipelines

This defines personal data, sensitive categories, and how AI systems collect and process them. It links data types to differential legal treatment.

Lesson 1 • Patent Law and AI-Assisted Invention

This covers inventorship doctrine, patentability of AI outputs, and disclosure requirements. It addresses the growing tension between AI autonomy and human-inventor rules.

Lesson 2 • Copyright in AI Training and Output

This analyses whether training on copyrighted data constitutes infringement and who owns AI-generated works. It directly shapes content and media AI product strategy.

Lesson 3 • Trade Secrets and Model Protection

This examines how trade secret law protects AI models, weights, and training datasets. It identifies misappropriation risks in employment and vendor relationships.

Lesson 1 • Product Liability Applied to AI Systems

This maps manufacturing defect, design defect, and failure-to-warn theories onto AI products. It establishes the primary liability framework for AI harm claims.

Lesson 2 • Emerging Strict Liability Proposals for AI

This reviews legislative and academic proposals for strict liability regimes targeting high-risk AI. It prepares you for a shifting liability landscape.

Lesson 3 • Autonomous Systems and Novel Liability Gaps

This addresses liability vacuums created by fully autonomous AI agents acting without human oversight. It explores doctrinal adaptations and legislative responses.

Lesson 4 • Negligence and Duty of Care for AI

This applies the negligence elements—duty, breach, causation, damages—to AI deployment decisions. It builds analytical skill for advising clients on risk mitigation.

Lesson 1 • Risk-Based Regulatory Architecture

This explains how regulators classify AI by risk level and impose proportionate obligations. It provides the structural logic underlying most current AI regulatory frameworks.

Lesson 2 • Transparency and Explainability Requirements

This covers mandatory disclosure, labeling, and explainability obligations imposed on AI systems. It links technical interpretability methods to legal compliance needs.

Lesson 3 • Sector-Specific AI Regulation

This surveys AI rules in financial services, healthcare, employment, and critical infrastructure. It shows how horizontal AI law interacts with vertical sector regulation.

Lesson 4 • Building an AI Compliance Program

This translates regulatory requirements into organisational policies, controls, and governance structures. It equips you to design and audit AI compliance programs.

Lesson 1 • High-Stakes AI Discrimination Domains

This applies discrimination analysis to hiring, lending, housing, and criminal justice AI. It illustrates how context shapes legal standards and remediation obligations.

Lesson 2 • Auditing AI Systems for Bias

This covers technical and legal audit methodologies for detecting and documenting algorithmic bias. It prepares you to commission, interpret, and act on AI audits.

Lesson 3 • Discrimination Law Fundamentals

This reviews protected characteristics, disparate treatment, and disparate impact theories. It establishes the doctrinal baseline for analysing AI discrimination claims.

Lesson 1 • Professional Responsibility and Competence

This applies attorney competence, supervision, and confidentiality duties to AI tool use. It identifies disciplinary risks and best practices for responsible AI adoption.

Lesson 2 • AI in Judicial Decision-Making

This analyses risk assessment tools, sentencing algorithms, and case prediction systems used by courts. It raises due process and equal protection concerns for judicial AI.

Lesson 3 • AI Evidence and Admissibility

This examines authentication, hearsay, and expert witness rules as applied to AI-generated evidence. It prepares you to challenge or defend AI evidence in litigation.

Lesson 4 • AI Tools in Legal Research and Drafting

This surveys generative AI, legal research platforms, and contract automation tools. It grounds professional responsibility analysis in concrete tool capabilities.

Lesson 1 • AI Ethics Frameworks and Legal Relevance

This maps ethical principles—fairness, accountability, transparency, safety—to legal obligations and reputational risk. It shows how ethics frameworks anticipate future regulation.

Lesson 2 • Contracting for AI Products and Services

You will draft and negotiate AI-specific contract provisions covering performance, liability, data rights, and audit. This is directly applicable to vendor, partnership, and procurement work.

Lesson 3 • Strategic Legal Counsel on AI Initiatives

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

Lesson 4 • Enterprise AI Governance Structures

This designs board-level oversight, AI review committees, and accountability mechanisms for AI deployment. It equips you to build governance infrastructure in organisations.