Chelsea AI Ventures
Business AI Training Strategist Pathway 6 Weeks (Online, approx. 4-5 hours/week) or 2 Day Intensive (In-Person, London)

Responsible AI in Practice: Governance, Risk, and Compliance

Navigate the complex landscape of AI ethics, regulation, and security to build trustworthy AI systems and protect your organization from significant risk

Target Audience

The AI Guardian

Core Value

Move from principles to practice by building and operationalizing a comprehensive Responsible AI program for your organization

Key Differentiator

A practical, hands-on approach focused on implementing actionable controls, policies, and compliance frameworks

Learning Objectives

  • Establish a comprehensive AI governance framework, including roles, responsibilities, and review boards
  • Conduct AI risk assessments, identifying potential ethical, legal, reputational, and operational risks
  • Develop strategies to detect and mitigate algorithmic bias in machine learning models
  • Navigate the key requirements of major AI regulations, including the EU AI Act
  • Identify and create mitigation plans for AI-specific security threats like model evasion and data poisoning
  • Create an operational roadmap for implementing and monitoring responsible AI practices

Prerequisites

A foundational understanding of AI concepts (as provided in 'AI Literacy') is recommended but not required.

Course Structure

Week 1: The Landscape of AI Risk & Responsibility

Define Responsible AI. Explore real-world case studies of AI failures and their business impact. Categorize AI risks: ethical, legal, reputational, and security.

Activities:

  • Conduct a preliminary AI risk mapping exercise for your organization

Week 2: Building the Foundations: AI Governance Frameworks

Learn the components of an effective AI governance program: principles, policies, procedures, and people. Design AI review boards and define roles.

Activities:

  • Draft a charter for an AI governance committee

Week 3: Technical Deep Dive: Fairness, Bias, and Explainability

Understand the sources of algorithmic bias. Explore technical methods for measuring fairness and tools for model explainability (XAI).

Activities:

  • Use an interactive tool to identify and measure bias in a sample dataset and model

Week 4: Navigating the Regulatory Maze: The EU AI Act & Global Compliance

A detailed walkthrough of the EU AI Act's risk-based approach. Discuss its global impact and other emerging compliance frameworks.

Activities:

  • Classify AI use cases according to the EU AI Act's risk tiers

Week 5: AI Security: New Threats, New Defenses

Explore the unique security vulnerabilities of AI systems, including prompt injection, data poisoning, and model evasion attacks. Learn about defensive strategies.

Activities:

  • Participate in a tabletop exercise to respond to a simulated AI security incident

Week 6: Operationalizing Responsible AI

Develop a strategic roadmap for rolling out a Responsible AI program. Focus on change management, training, and creating documentation and impact assessments.

Activities:

  • Build a Responsible AI implementation plan for a specific project

Topics Covered

AI Governance Frameworks
Corporate Risk Management for AI
Algorithmic Bias and Fairness
Explainable AI (XAI)
EU AI Act and Compliance
AI Security and Threat Modeling
Data Privacy in AI Systems
AI Impact Assessments
Responsible AI Principles
AI Ethics Committees
Regulatory Technology (RegTech)
Trustworthy AI

Capstone Project

Develop a comprehensive AI Governance and Risk Mitigation plan for a new, high-risk AI initiative within a regulated industry.

Why This Course Matters

As AI becomes deeply integrated into business operations, the risks—reputational, legal, and financial—are escalating. A single biased algorithm or security breach can have catastrophic consequences. This course is designed for the leaders who are responsible for protecting the organization by ensuring that AI is developed and deployed responsibly, ethically, and securely.

What Makes This Course Different

Many courses discuss AI ethics in the abstract. This program is relentlessly practical. It provides the specific frameworks, checklists, and knowledge needed to build and enforce an AI governance program. You will learn not just what the risks are, but how to manage them through concrete policies, technical controls, and compliance procedures.

Course Philosophy

We believe that trust is the ultimate currency in the AI era. Trustworthy AI is not an accident; it is the result of deliberate design, rigorous governance, and continuous oversight. This course equips you with the tools to build that trust into every AI system your organization deploys.

Who Should Take This Course

This course is essential if you:

  • Are responsible for corporate governance, risk, or compliance
  • Lead legal or privacy teams navigating new technology
  • Are a technology leader deploying AI in sensitive or regulated areas
  • Need to understand and prepare for regulations like the EU AI Act
  • Want to build AI systems that are not just powerful, but also trustworthy

Ready to transform your team?

Contact us to discuss custom training solutions or group enrollment options.

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