An Interactive 5-Day Training Course

Artificial Intelligence (AI) Ethics, Regulations & Compliance

Ensuring Responsible, Transparent, and Compliant AI Across the Enterprise

08 - 12 Dec 2025

London

| $5950

04 - 08 May 2026

London

| $5950

24 - 28 Aug 2026

Amsterdam

| $5950

12 - 16 Oct 2026

Paris

| $5950

23 - 27 Nov 2026

Milan

| $5950

07 - 11 Dec 2026

London

| $5950

Introduction

This GLOMACS Artificial Intelligence (AI) Ethics, Regulations & Compliance training course provides a comprehensive and practical foundation for governing AI responsibly across modern organisations. As AI systems become embedded in decision-making, operations, customer interactions, and automated workflows, leaders must ensure these technologies are transparent, safe, fair, and aligned with emerging global standards. This training course addresses the ethical, regulatory, and compliance challenges experienced by organisations adopting advanced AI-driven solutions.

Through real-world examples, global regulatory updates, ethical governance models, and organisational governance practices, participants will gain clarity on how to design, evaluate, deploy, and monitor AI systems responsibly. The training course focuses on addressing algorithmic bias, maintaining accountability in automated decisions, ensuring transparency and explainability, implementing governance controls, and complying with international AI regulations such as the EU AI Act, NIST AI Risk Management Framework, OECD AI Principles, and regional data protection laws.

Core principles of ethical and responsible AI
Understanding global AI regulations and compliance expectations
Governance frameworks for safe, transparent, and accountable AI use
Identifying and mitigating algorithmic bias and unintended harm
Designing compliant AI processes that protect people, data, and organisations

Key Learning Outcomes

At the end of this training course, you will learn to:

Understand global AI ethics principles and regulatory requirements
Apply governance frameworks for responsible AI implementation
Identify and mitigate algorithmic bias, fairness issues, and unintended impacts
Evaluate AI risks and develop transparent, accountable processes
Develop compliance strategies aligned with organisational and legal obligations

Training Methodology

This training course uses an applied and interactive methodology that combines expert-led discussions, ethical evaluation exercises, regulatory interpretation, governance scenario analysis, and guided reflection. Participants will explore practical cases of AI failures, review global regulatory frameworks, evaluate risk controls, and design actionable compliance measures aligned with their organisational context.

Artificial Intelligence (AI) Ethics, Regulations & Compliance

Who Should Attend?

Organisations will gain stronger AI governance and compliance maturity through:

Reduced legal, ethical, and reputational risks
Stronger alignment with global AI regulations and standards
Transparent and accountable AI implementation processes
Reduced algorithmic errors and unintended consequences
Enhanced trust among customers, employees, and stakeholders
Improved readiness for future AI audits and regulatory assessments

Learning Journey Breakdown

Understanding the need for AI ethics in modern organisations
Core principles: fairness, transparency, accountability, and human oversight
Ethical risks across machine learning, automation, and predictive analytics
Distinguishing between ethical AI, lawful AI, and trustworthy AI
Introduction to global AI ethics frameworks (OECD, UNESCO, NIST)
Challenges of implementing ethical principles in real-world environments

Overview of global AI regulatory landscape
Deep dive: EU AI Act, classification levels, and obligations
Understanding NIST AI Risk Management Framework
AI-related GDPR considerations: data minimisation, profiling, consent
Requirements for transparency, documentation, and human oversight
Preparing for future regulatory developments across regions

Identifying sources of bias in datasets, models, and deployment
Evaluating fairness metrics and model performance
Assessing harm, unintended consequences, and discriminatory outcomes
Model transparency and explainability expectations
AI risk assessment techniques and risk scoring
Implementing mitigation controls across the AI lifecycle

Creating an organisational AI governance structure
Defining roles, responsibilities, and accountability lines
Documentation, audit trails, and lifecycle monitoring
Establishing model validation and approval processes
Responsible use policies, escalation procedures, and oversight controls
Embedding compliance into procurement and third-party AI systems

Developing internal ethical standards and behaviour guidelines
Training and upskilling employees for AI literacy and governance
Managing change: communicating risks, responsibilities, and expectations
Integrating AI governance with corporate strategy and ESG commitments
Preparing for internal and external AI audits
Creating a long-term roadmap for responsible AI maturity

Ready to Take the Next Step?

Reserve your slot today and start your learning journey with us.

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