AI Governance Lawyers - Program Module Details

Here are the detailed, standalone descriptions for each course within Modules 1, 2 and 3. These are designed for clarity on the specific learning outcomes for each session.

These **descriptions are specifically mapped to the Law Society of Ontario (LSO) and Law Society of British Columbia (LSBC) requirements for Professionalism and Equality, Diversity, and Inclusion (EDI) credits

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The CPD Credit Breakdown

This program is meticulously architected to meet the mandatory annual requirements of the Law Society of Ontario (LSO) and the Law Society of British Columbia (LSBC).

• This program contains 2 Hours Professionalism-Ethics.

• This program contains 1 Hour EDI (Equality, Diversity, and Inclusion).

• This program is eligible for up to 9 Substantive Hours.

• Total Bundle Value: 12 CPD Hours.

Module 1: Ethics, Professionalism, and EDI in the AI Era

Duration: 3 Hours | Focus: Governance & Ethics

• Course 1.1: AI Ethics and Professional Responsibility (2 Hours Professionalism) Focuses on the Duty of Technological Competence, data sovereignty in cloud-based LLMs, and the professional duty to supervise AI-generated work product to prevent "hallucinations."

• Course 1.2: Algorithmic Bias and Inclusive Justice (1 Hour EDI) Explores how to identify and mitigate systemic bias in legal algorithms and ensures AI-driven tools promote, rather than hinder, equitable access to justice.

Module 2: AI Tools, Platforms, and Prompt Engineering

Duration: 3 Hours | Focus: Practical Skills & Workflow

• Course 2.1: The Lawyer’s AI Tech Stack (1.5 Substantive Hours) A comparative analysis of legal AI platforms (CoCounsel, Harvey, Lexis+ AI) and a framework for conducting vendor security due diligence.

• Course 2.2: Advanced Prompt Engineering for Lawyers (1.5 Substantive Hours) Hands-on mastery of the Context-Persona-Task-Constraint framework to generate high-quality, legally sound drafts and research.

Module 3: AI in Substantive Law Practice

Duration: 6 Hours | Focus: Applied Substantive Law

• Course 3.1: AI in Administrative Law (2 Substantive Hours) Analyzes the standard of review for automated government decisions and the "Right to an Explanation" in tribunal settings.

• Course 3.2: AI in Criminal Law (2 Substantive Hours) Addresses Charter (Section 7 & 8) implications regarding predictive policing, digital evidence, and algorithmic risk assessments.

• Course 3.3: AI in Civil Law & Litigation (2 Substantive Hours) Modernizing e-Discovery via Technology Assisted Review (TAR) and exploring civil liability for AI-driven errors.

Synergistic Bundle Learning

Each module in this bundle builds logically on the last. You move from the foundational "should-we" of legal ethics (Module 1) to the practical "how-to" of AI tool mastery (Module 2), finally applying both skill sets to the high-stakes environments of Administrative, Criminal, and Civil Law (Module 3).

Module 1: Ethics, Professionalism, and EDI in the AI Era

Focus: The Governance and "Should-We" of Modern Practice.

Course 1.1: AI Ethics and Professional Responsibility

Duration: 2 Hours (2 Professionalism Hours) This course addresses the fundamental shift in a lawyer's ethical obligations in an AI-saturated landscape. It provides a roadmap for maintaining professional integrity and protecting solicitor-client privilege while utilizing automated systems.

• Key Learning Outcomes:

  • The Duty of Technological Competence: Understanding the LSO/LSBC mandate to maintain a functional knowledge of the risks and benefits of the technology used in your practice.

  • Confidentiality & Data Sovereignty: How to prevent "data leakage" into public AI models and ensuring your firm's cloud-based LLMs meet professional secrecy standards.

  • The Duty of Supervision: Establishing internal firm protocols for reviewing AI-generated work product (drafting, research, and analysis) to prevent "hallucinations" and factual errors.

  • Client Disclosure & Transparency: Developing clear, defensible retainer clauses and communication strategies to inform clients of AI involvement in their matters.

Course 1.2: Algorithmic Bias and Inclusive Justice

Duration: 1 Hour (1 EDI Hour) As AI systems are trained on historical data, they often inherit and amplify systemic biases. This session explores the lawyer's role in ensuring that "Digital Justice" remains equitable and accessible to all members of society.

• Key Learning Outcomes:

  • Identifying Algorithmic Bias: Understanding how "black box" algorithms in research, hiring, and predictive tools can perpetuate discrimination against marginalized groups.

  • Mitigation Strategies: Practical steps for auditing the AI tools your firm uses to ensure they do not produce discriminatory outcomes or violate human rights codes.

  • Inclusive Client Service: Using AI to bridge the "Access to Justice" gap, ensuring that automated tools are designed to assist, not exclude, diverse populations.

  • Ethics of Data Sets: Challenging the validity of automated evidence or research that is built on non-representative or biased data.

Module 2: AI Tools, Platforms, and Prompt Engineering

Focus: The Practical "How-To" of Digital Practice.

Course 2.1: The Lawyer’s AI Tech Stack

Duration: 90 Minutes (1.5 Substantive Hours) This course provides a high-level comparative analysis of the primary AI platforms currently reshaping the legal industry. Rather than focusing on a single vendor, we provide a framework for evaluating tools based on security, utility, and cost-efficiency.

• Key Learning Outcomes:

  • Comparative review of "Big Tech" legal integrations (e.g., Lexis+ AI, Westlaw Precision) vs. specialized boutique AI platforms (e.g., CoCounsel, Harvey).

  • Understanding the difference between closed-loop legal AI and public-facing Generative AI (ChatGPT/Claude) for data privacy.

  • Practical strategies for performing vendor due diligence and security auditing to meet Law Society requirements.

  • Implementing AI into Contract Lifecycle Management (CLM) to automate intake and initial redlining.

Course 2.2: Advanced Prompt Engineering for Legal Professionals

Duration: 90 Minutes (1.5 Substantive Hours) Prompting is the new "legal research" skill. This session transitions from basic queries to sophisticated "Chain-of-Thought" prompting, ensuring that the outputs you receive from AI are high-quality, legally sound, and require minimal editing.

• Key Learning Outcomes:

  • Master the Context-Persona-Task-Constraint framework specifically for legal drafting.

  • Learn to "prompt-engineer" complex tasks: case law summaries, legislative cross-referencing, and initial memo drafting.

  • Hands-on practical lab using anonymized case patterns to practice iterative refinement (getting the AI to "think through" a problem rather than just summarizing).

  • Techniques for identifying and correcting AI "hallucinations" through verification prompts.

Module 3: AI in Substantive Law Practice

Focus: Specialized Application in High-Stakes Legal Domains.

Course 3.1: AI in Administrative Law

Duration: 2 Hours (2 Substantive Hours) AI is increasingly being used by government agencies and tribunals for automated decision-making. This course explores the standard of review and the procedural fairness requirements when a government body uses an algorithm to impact a client's rights.

• Key Learning Outcomes:

  • Analyzing the "Right to an Explanation" and how it impacts judicial reviews of automated decisions.

  • Understanding the standard of review for AI-assisted rulings in administrative tribunals.

  • Compliance with Canada’s Artificial Intelligence and Data Act (AIDA) in a regulatory context.

Course 3.2: AI in Criminal Law

Duration: 2 Hours (2 Substantive Hours) The intersection of technology and liberty presents unique challenges. This course focuses on the evidentiary and Charter implications of AI in the criminal justice system.

• Key Learning Outcomes:

  • Charter Section 7 & 8: Privacy and fair trial implications of predictive policing and mass data surveillance.

  • Challenging the validity of algorithmic evidence (e.g., facial recognition or pattern-matching software) in court.

  • Understanding the role of AI in bail assessments and sentencing risk profiles.

Course 3.3: AI in Civil Law & Litigation

Duration: 2 Hours (2 Substantive Hours) Modern litigation is defined by the volume of data. This course focuses on scaling your civil practice through automated discovery and predictive analytics.

• Key Learning Outcomes:

  • Technology Assisted Review (TAR): Implementing e-Discovery workflows that satisfy judicial expectations for production.

  • Predictive Analytics: Using data tools to forecast judicial leanings, settlement ranges, and litigation risks.

  • Liability Frameworks: Who is at fault when AI fails? Exploring tort and contract liability for AI errors in a civil context.

Strategic Value of These Descriptions

These descriptions ensure that Registrants know exactly which competencies are being addressed. This clarity is essential for:

1. Selection: Helping practitioners choose individual modules that fill their specific knowledge gaps.

2. Audit Support: Providing the Law Society/Regulator with a clear, detailed syllabus of the substantive law covered.