AI Law compliance training course

Lesson 1 • Stakeholder Roles in AI Governance

Identifies developers, deployers, users, and regulators as distinct legal actors. Clarifies how role assignment determines compliance duties throughout the AI lifecycle.

Lesson 2 • Core Legal Principles Governing AI

Introduces foundational legal principles—accountability, transparency, fairness—as applied to AI. Connects abstract principles to concrete compliance obligations professionals will encounter.

Lesson 3 • What Makes AI Legally Distinct

Examines the technical properties of AI that create novel legal challenges. Establishes why traditional legal categories inadequately address autonomous, adaptive systems.

Lesson 4 • Global Regulatory Landscape Overview

Maps the major regulatory approaches across jurisdictions without citing specific codes. Enables professionals to recognize convergent and divergent regulatory philosophies worldwide.

Lesson 1 • Conducting an AI Risk Assessment

Provides a step-by-step methodology for assessing probability, severity, and breadth of AI-related harms. Produces a documented risk profile usable in regulatory submissions.

Lesson 2 • Risk Register Maintenance

Establishes ongoing risk register practices to track evolving AI system risks over time. Links register updates to change management and regulatory reporting cycles.

Lesson 3 • Bias and Discrimination Risk

Focuses on algorithmic bias as a distinct legal risk category requiring dedicated analysis. Connects bias detection methods to anti-discrimination compliance obligations.

Lesson 4 • Risk-Tier Frameworks Explained

Deconstructs how regulators categorize AI into risk tiers from minimal to unacceptable. Grounds classification logic in the harm potential and context of deployment.

Lesson 1 • Data Minimization and Purpose Limitation

Applies data minimization and purpose limitation principles to AI dataset design. Reduces regulatory exposure by aligning data collection scope with declared processing purposes.

Lesson 2 • Lawful Bases for AI Data Processing

Examines the legal grounds that justify processing personal data in AI training and inference. Guides professionals in selecting and documenting the appropriate lawful basis.

Lesson 3 • Personal Data in AI Systems

Defines personal data, sensitive data, and inferred data in the context of AI processing. Establishes why AI amplifies privacy risks compared to conventional data processing.

Lesson 4 • Individual Rights in AI Contexts

Covers data subject rights—access, correction, erasure, portability—as they apply to AI outputs. Addresses the technical challenges of honoring rights in trained model environments.

Lesson 1 • AI System Documentation Requirements

Details the technical and operational documentation regulators require for AI systems. Establishes documentation as a compliance asset that supports audits and incident response.

Lesson 2 • Meaningful Explanation to Individuals

Focuses on crafting explanations that satisfy the legal standard of being meaningful to non-experts. Covers format, language, and content requirements for individual-facing notices.

Lesson 3 • Regulatory Transparency Requirements

Surveys mandatory disclosure obligations imposed on AI developers and deployers. Distinguishes between transparency to regulators, affected individuals, and the general public.

Lesson 4 • Disclosure in Automated Decisions

Addresses the specific disclosure duties triggered when AI makes or significantly influences decisions. Connects disclosure obligations to the right to contest automated outcomes.

Lesson 1 • Incident Detection and Response

Establishes processes for detecting, classifying, and responding to AI-related incidents. Meets regulatory requirements for incident logging, notification, and corrective action.

Lesson 2 • Accountability Mapping and Role Assignment

Creates clear accountability maps linking AI system functions to named responsible roles. Prevents accountability gaps that regulators identify as systemic compliance failures.

Lesson 3 • Designing Human-in-the-Loop Processes

Provides frameworks for embedding human review at critical AI decision points. Balances operational efficiency with the depth of oversight required by risk level.

Lesson 4 • Audit Trails and Accountability Records

Specifies the records needed to demonstrate accountability to regulators and courts. Covers retention periods, integrity controls, and access management for audit logs.

Lesson 1 • Managing the Certification Lifecycle

Addresses ongoing obligations after initial certification, including surveillance and renewal. Prevents compliance drift by embedding certification maintenance into change management.

Lesson 2 • Testing and Validation Requirements

Specifies the testing standards AI systems must meet before receiving regulatory approval. Links validation methodology to the specific risk categories identified in earlier chapters.

Lesson 3 • Technical File Preparation

Guides professionals through assembling the technical documentation package required for assessment. Covers mandatory content, evidence standards, and common deficiencies that delay approval.

Lesson 4 • Standards and Harmonized Specifications

Explains how voluntary technical standards create presumption of conformity with legal requirements. Guides selection and application of relevant standards to streamline assessment.

Lesson 1 • Responding to Regulatory Investigations

Provides a structured approach to managing regulatory investigations from first contact to resolution. Emphasizes cooperation strategies that reduce penalty exposure and preserve relationships.

Lesson 2 • Civil Liability for AI Harms

Examines tort and product liability theories applicable to AI-caused harm. Connects liability exposure to the risk assessment and documentation practices covered in prior chapters.

Lesson 3 • Building a Defensible Compliance Record

Identifies the documentation and process evidence that demonstrates good-faith compliance efforts. Translates enforcement lessons into proactive record-keeping and governance improvements.

Lesson 4 • Regulatory Enforcement Mechanisms

Surveys the investigative and enforcement tools available to AI regulators. Prepares professionals to recognize enforcement triggers and respond appropriately to regulatory contact.

Lesson 1 • Reporting to Leadership and Regulators

Develops reporting frameworks that keep executives, boards, and regulators informed of compliance status. Ensures reporting content meets both internal governance needs and external regulatory expectations.

Lesson 2 • Monitoring, Auditing, and Continuous Improvement

Establishes ongoing monitoring and internal audit functions to detect and correct compliance gaps. Embeds continuous improvement cycles that adapt the program to regulatory and technological change.

Lesson 3 • Compliance Program Design and Governance

Synthesizes regulatory requirements into a coherent compliance program architecture. Establishes governance bodies, charters, and reporting lines that give the program organizational authority.

Lesson 4 • Training and Awareness Programs

Designs role-based training programs that build compliance competency across the organization. Links training completion to accountability records and regulatory demonstration of due diligence.