AI+ Ethics Fundamentals™

USD$195.00 (GST excl.)

Formerly known as AI+ Ethics™

Navigate the Intersection of AI and Ethics in Business Landscape

Responsible AI Focus: Master ethical AI use aligned with business and societal values

Risk Mitigation: Learn to manage compliance, transparency, and AI decision-making

Strategic Guidance: Integrate ethical practices into AI adoption and leadership

Reputation Builder: Build organisational trust and credibility in AI deployments

For more information, see below

Duration

Instructor-Led: 1 day (live or virtual)
Self-Paced: 8 hours of content

Format

Online, self-paced

Exam

50 questions, 70% passing, 90 minutes, online proctored exam

Overview

Responsible AI Focus: Master ethical AI use aligned with business and societal values

Risk Mitigation: Learn to manage compliance, transparency, and AI decision-making

Strategic Guidance: Integrate ethical practices into AI adoption and leadership

Reputation Builder: Build organisational trust and credibility in AI deployments

Prerequisites

Basic knowledge of artificial intelligence, machine learning concepts, Python familiarity, fundamental AI/ML concepts

What you’ll learn

Ethics Professionals:Enhance your expertise in AI ethics to guide responsible AI deployment.
AI & Data Enthusiasts:Learn how to apply ethical frameworks in AI decision-making processes.
Compliance Officers:Ensure AI technologies comply with legal and ethical standards to mitigate risks.
Technology Leaders:Drive ethical AI strategies and lead responsible AI initiatives within organizations.
Students & New Graduates:Gain a competitive edge in the rapidly growing field of AI ethics.

Why this certification matters

In-Depth Ethical Understanding: Understand ethical considerations and social impacts of AI for responsible decision-making.
Bias Mitigation and Fairness: Learn strategies to identify and prevent biases in AI systems, ensuring fairness and transparency.
Privacy and Security Assurance: Explore strategies to safeguard privacy and secure AI systems and data.
Legal and Regulatory Compliance: Understand global AI regulations to ensure compliance with legal and ethical standards.

Tools you’ll use

AI4People (Atomium – European Institute for Science, Media, and Democracy)

IBM – AI Fairness 360

IBM – AI Explainability 360

European Commission High-Level Expert Group on AI

Learning Modules

10 modules

1Course Overview

Course Introduction Preview

2Module 1: Foundations of AI Ethics and Responsible AI

1.1 Understanding AI in a Modern Ethics Context
1.2 The Societal Impact of AI Technologies
1.3 Core Principles and Stakeholders
1.4 Building AI Literacy for the Workplace
1.5 Human Rights, Democracy, and AI Ethics
1.6 Case Studies

3Module 2: Bias, Fairness, and Inclusion in AI

2.1 Where Bias Enters AI Systems
2.2 Fairness Concepts and Practical Evaluation
2.3 Mitigation and Inclusive Design
2.4 Applied Fairness Cases
2.5 Case Studies

4Module 3: Transparency, Explainability, and Documentation

3.1 Why Transparency Matters
3.2 Explainability Methods and Documentation Standards
3.3 Communicating AI Decisions Responsibly
3.4 Transparency, Documentation, and Governance Practices
3.5 Case Studies

5Module 4: Privacy, Security, and AI Data Governance

4.1 Privacy Principles in AI
4.2 AI Data Governance and Data Quality
4.3 Security Risks in AI Systems
4.4 Privacy-Preserving AI Techniques
4.5 Content Authenticity, Provenance, and Trust
4.6 Real World Case Studies

6Module 5: Accountability, Oversight, and AI Governance

5.1 Accountability Across the AI Lifecycle
5.2 Human Oversight and Control
5.3 Risk Management and Assurance
5.4 Red Teaming and Safety Testing
5.5 Governance Operating Model
5.6 Grievance and Remedy Processes
5.7 System Retirement and Decommissioning
5.8 Applied Case Studies

7Module 6: Legal, Regulatory, and Standards Landscape

6.1 International Principles and Treaties
6.2 Management and Technical Standards
6.3 Binding Regional Laws
6.4 National Guidance and Voluntary Frameworks
6.5 Sector-Specific and Cross-Border Compliance
6.6 Case Studies

8Module 7: Generative AI, Agentic AI, and Responsible Deployment

7.1 How Modern Generative and Agentic AI Systems Work
7.2 New Risks Introduced by Generative AI
7.3 Agentic AI Risks and Governance
7.4 Evaluation and Safe Deployment
7.5 Responsible Use Cases and Boundaries

9Module 8: Capstone – AI Ethics Impact Assessment and Governance Plan

8.1 Select an AI Use Case
8.2 Perform an Ethics and Risk Assessment
8.3 Develop an AI Governance Package Using the NIST AI RMF
8.4 Final Capstone Deliverable
8.5 Review and Reflection

10Optional Module: AI Agents for Ethics

1.1 What Are AI Agents?
1.2 Applications and Trends of AI Agents for Ethics
1.3 How Does an AI Agent Work?
1.4 Core Characteristics of AI Agents
1.5 Importance of AI Agents
1.6 Types of AI Agents

Format	Online, self-paced
Duration	Instructor-Led: 1 day (live or virtual) Self-Paced: 8 hours of content
Exam	50 questions, 70% passing, 90 minutes, online proctored exam
Included	Instructor-led OR Self-paced course + Official exam + Digital badge
Prerequisites	Basic knowledge of artificial intelligence, machine learning concepts, Python familiarity, fundamental AI/ML concepts
Delivery	Projects & case studies
Outcome	Industry-recognized credential + hands-on experience

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