Introduction

As artificial intelligence becomes deeply embedded in organisational decision-making, automation, and customer engagement, ethical responsibility and regulatory compliance are no longer optional. Poorly governed AI systems can introduce bias, legal exposure, reputational damage, and loss of stakeholder trust. Organisations must therefore balance innovation with accountability, transparency, and control.

The AI Ethics, Regulations & Compliance Course provides a structured and practical understanding of how to govern AI responsibly across the enterprise. It explores global AI regulations, ethical principles, and governance models that support safe, fair, and explainable AI use. By integrating ethics, compliance, and risk management into AI lifecycles, the course enables organisations to deploy AI confidently while meeting evolving legal and societal expectations.

Key focus areas include:

Key Learning Outcomes

At the end of this training course, participants will be able to:

Training Methodology

The AI Ethics, Regulations & Compliance Course follows an applied, expert-led learning approach that combines governance discussion, ethical evaluation, and regulatory interpretation. Participants analyse real-world AI cases, assess compliance requirements, and explore governance scenarios to translate ethical and regulatory principles into actionable enterprise AI controls.

Artificial Intelligence (AI) Ethics, Regulations & Compliance

Who Should Attend?

This training course is ideal for professionals seeking to strengthen ethical and regulatory oversight of AI, including:

  • Risk, Compliance, and Governance Professionals
  • Legal and Regulatory Affairs Specialists
  • AI, Data, and Digital Transformation Leaders
  • Internal Audit and Assurance Professionals
  • Senior Managers responsible for AI strategy
  • Policy and Ethics Committee Members

Course Outline

Day 1

Foundations of Ethical and Responsible AI

  • 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
Day 2

Global AI Regulations and Emerging Compliance Standards

  • 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
Day 3

AI Risk, Bias, Fairness, and Mitigation Strategies

  • 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
Day 4

Designing AI Governance and Compliance Systems

  • 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
Day 5

Building a Responsible AI Culture and Future Readiness

  • 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

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Related Categories

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Governance Courses:
Board · Ethics · Accountability
Compliance Courses:
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AI · Data · Cyber

FAQs

The course focuses on governing AI responsibly by integrating ethics, regulatory compliance, and risk management into AI lifecycles. It helps organisations ensure AI systems are transparent, fair, accountable, and aligned with global regulatory expectations.

Yes, the AI Ethics, Regulations & Compliance Course examines key global frameworks including the EU AI Act, NIST AI Risk Management Framework, and OECD AI Principles. Participants gain clarity on how these regulations affect AI design, deployment, and organisational governance.

The course explores sources of bias across data, models, and deployment processes. It provides practical approaches for identifying, assessing, and mitigating fairness risks to reduce unintended harm and discriminatory outcomes.

Absolutely. The AI Ethics, Regulations & Compliance Course is highly relevant for organisations operating AI systems that require stronger governance, compliance controls, and audit readiness. It also supports future AI initiatives by embedding ethical and regulatory considerations early.

Yes, participants learn how to design organisational AI governance frameworks, define accountability roles, and establish oversight mechanisms. These structures help ensure continuous compliance, transparency, and responsible AI use across the enterprise.

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