AI+ Ethics Fundamentals™

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The AI+ Ethics Fundamentals™ certification provides professionals with a comprehensive understanding of the ethical, social, legal and governance challenges associated with artificial intelligence.

The course explores responsible AI, bias and fairness, transparency and explainability, privacy and security, accountability, regulatory compliance, ethical decision-making and AI governance. Participants learn how to assess the social and ethical impact of AI systems, develop responsible AI practices and apply global standards to real-world situations. Through guided projects, case studies and practical exercises, the certification prepares professionals to support trustworthy, transparent and responsible AI adoption within organisations.

Duration

Self-Paced: Approximately 8 hours of on-demand content.
Includes approximately 8 hours of on-demand video lessons, an e-book and podcasts.
Modular quizzes are included to help learners track their progress.

Who Should Attend

Ethics Professionals: Enhance expertise in AI ethics and support responsible AI development and deployment.
AI & Data Enthusiasts: Learn how to apply ethical frameworks to AI development and decision-making.
Compliance Officers: Ensure AI technologies comply with legal and ethical standards and help mitigate associated risks.
Technology Leaders: Develop ethical AI strategies and lead responsible AI initiatives within organisations.
Students & New Graduates: Gain a competitive foundation in the rapidly growing field of AI ethics.

Prerequisites

Basic knowledge of artificial intelligence, machine learning concepts and their applications.
An understanding of the social, cultural and political implications of AI technologies.
An understanding of professional ethics, including honesty, integrity and responsibility.
Exposure to real-world AI case studies and ethical dilemmas.
The ability to critically assess AI technologies and make ethical decisions when designing, deploying and managing AI systems.
Familiarity with relevant laws, regulations and industry standards governing AI use.

Course Content

Course Overview

Course Introduction

Module 1: Overview of AI Ethics & Societal Impact

1.1 Introduction to Ethical Considerations in AI
1.2 Understanding the Societal Impact of AI Technologies
1.3 Strategies for Conducting Social and Ethical Impact Assessments

Module 2: Bias and Fairness in AI

2.1 Exploration of Biases in Data and Algorithms
2.2 Strategies for Mitigating Bias and Ensuring Fairness in AI Systems

Module 3: Transparency and Explainable AI

3.1 Importance of Transparent AI Systems
3.2 Techniques for Explaining AI Models to Diverse Stakeholders
3.3 Guided Projects on Designing and Analysing AI Systems with Ethical Considerations

Module 4: Privacy and Security Issues in AI

Study frameworks for holding organisations accountable for the ethical use of AI.
Understand why privacy and security are essential for ethical AI deployment.
Explore approaches to mitigating the consequences of potential AI misuse or harm.

Module 5: Accountability and Responsibility

5.1 Concepts of Accountability in AI Development and Deployment
5.2 Responsibilities of AI Practitioners and Organisations

Module 6: Legal and Regulatory Issues

6.1 Overview of Relevant Laws and Regulations Pertaining to AI
6.2 Understanding the Global Regulatory Issues for AI Technologies
6.3 Case Studies: GDPR Compliance
6.4 Legal Compliance of AI Tools

Module 7: Ethical Decision-Making Frameworks

7.1 Introduction to Frameworks for Making Ethical Decisions in AI
7.2 Case Studies and Applications of Ethical Decision-Making
7.3 Use of Simulation Platforms in Ethical Decision-Making

Module 8: AI Governance & Best Practices

8.1 Principles and Functions of International AI Governance
8.2 Best Practices for Integrating AI Ethics into Organisational Policies
8.3 Case Studies on AI Governance

Module 9: Global AI Ethics Standards

9.1 Explore Standards: IEEE’s Ethically Aligned Design
9.2 Comparative Case Studies on Standard Implementations
9.3 Tools for Evaluating AI Systems Against Global Standards

Optional Module: AI Agents for Ethics and Its Implications

Understanding AI Agents
Case Studies
Hands-On Practice with AI Agents

Tools You’ll Explore

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

Why This Certification Matters

Responsible AI Adoption: Develop the knowledge needed to integrate ethical considerations into AI development and deployment.
Bias Mitigation and Fairness: Learn strategies for identifying and reducing bias in AI systems and promoting fair, transparent outcomes.
Privacy and Security: Understand approaches to protecting personal information and securing AI systems and data.
Regulatory Compliance: Develop knowledge of global AI regulations and standards to support responsible and compliant AI adoption.
AI Governance: Learn how to establish governance practices, accountability and organisational policies for ethical AI.
Trust and Reputation: Help organisations build trust and credibility by ensuring AI systems are transparent, responsible and aligned with societal values.

Skills You’ll Gain

AI Ethics Principles
Risk Mitigation Strategies
Ethical AI Application Design
Fairness in AI Systems
Decision-Making in AI Governance
Regulatory and Compliance Knowledge
Ethical Leadership in AI
Impact Assessment of Ethical Violations

Job Roles & Industry Outlook

Ethics Advisor: Advise organisations on ethical issues associated with AI development and deployment, supporting justice, openness and accountability.
Policy Developer: Formulate and implement ethical and regulatory frameworks for responsible and equitable AI adoption.
Ethical Standards Advisor: Provide guidance on ethical considerations in AI development and deployment and ensure alignment with ethical principles and societal values.
Audit Architect: Design and implement systems for ethical compliance and accountability in AI applications, including audits to identify and mitigate ethical risks.

Exam Details

Duration: 90 minutes
Passing Score: 70% (35/50)
Format: 50 multiple-choice/multiple-response questions
Delivery Method: Online via a proctored exam platform with flexible scheduling.