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.

At a Glance: Course + Exam Overview

Program Name
AI+ Ethics Fundamentals™
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
Exam Format
50 questions, 70% passing, 90 minutes, online proctored exam
Delivery
Online labs, projects, case studies
Outcome
Industry-recognized credential + hands-on experience
Who Should Enroll?

Who Should Enroll?

  • 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.

Skills You'll Gain

AI Ethics PrinciplesRisk Mitigation StrategiesEthical AI Application DesignFairness in AI SystemsDecision-Making in AI GovernanceRegulatory and Compliance KnowledgeEthical Leadership in AIImpact Assessment of Ethical Violations

What You'll Learn

  • 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

  • Course Introduction Preview

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

Tools You'll Master

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

Prerequisites

  • AI and ML Basics: Understand basic AI, machine learning, generative AI, and automated decision-making concepts.
  • Digital and Data Literacy: Recognize how data is collected, processed, shared, and used by AI systems.
  • Ethics and Responsible AI Awareness: Have a basic understanding of fairness, transparency, accountability, privacy, and human oversight.
  • Business and Workplace Awareness: Understand how AI may support decisions, workflows, customer services, and organizational processes.
  • Risk and Governance Basics: Be familiar with policies, compliance, stakeholder impact, human rights, and responsible technology use.

Exam Details

Duration

90 minutes

Passing Score

70% (35/50)

Format

50 multiple-choice/multiple-response questions

Delivery Method

Online via proctored exam platform (flexible scheduling)

Exam Blueprint:

  • Foundations of AI Ethics and Responsible AI – 7%
  • Bias, Fairness, and Inclusion in AI – 15%
  • Transparency, Explainability, and Documentation – 15%
  • Privacy, Security, and AI Data Governance – 15%
  • Accountability, Oversight, and AI Governance – 12%
  • Legal, Regulatory, and Standards Landscape – 12%
  • Generative AI, Agentic AI, and Responsible Deployment – 12%
  • Capstone - AI Ethics Impact Assessment and Governance Plan – 12%

Choose the Format That Fits Your Schedule

What's Included (One-Year Subscription + All Updates):

  • High-Quality Videos, E-book (PDF & Audio), and Podcasts
  • AI Mentor for Personalized Guidance
  • Quizzes, Assessments, and Course Resources
  • Online Proctored Exam with One Free Retake
  • Comprehensive Exam Study Guide
  • Access for Tablet & Phone
Instructor-Led (Live Virtual/Classroom)
  • 1 day (live or virtual) Self-Paced: 8 hours of content
  • Real-time Q&A, peer collaboration, and hands-on labs
  • Led by AI Certified Trainers and delivered through Authorized Training Partners
Self-Paced Online
  • 8 hours of content
  • Learn anywhere, anytime, with modular quizzes to track progress

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Frequently Asked Questions

Organizations increasingly recognize the importance of ethical considerations in AI to mitigate risks, ensure fair decision-making, and maintain a positive brand image.

This certification is ideal for business leaders, AI developers, and ethics professionals in both business and government sectors. It is particularly beneficial for those looking to enhance their understanding of ethical AI practices and make informed decisions in AI deployment.

Obtaining the AI+ Ethics Fundamentals™ certification demonstrates a commitment to ethical AI practices, enhancing personal credibility and professional development. It equips individuals with the skills needed to navigate complex ethical aspects of AI, promoting responsible innovation and building public trust in AI technologies.

Professionals with this certification are recognized for their expertise in ethical AI practices, making them valuable assets to organizations and potentially leading to career growth opportunities.

Organizations with certified employees can better navigate the complexities of AI implementation, ensuring ethical practices are followed, which can protect and enhance their reputation and operational integrity.

The course is designed for flexibility, allowing participants to learn at their own pace, making it possible to complete based on individual schedules.

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