AI+ Ethics Fundamentals™
Formerly known as AI+ Ethics™ <br> <br> 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
Duur: 1 dag(en)
- 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.
- 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.
Inhoud
Course Overview
- Course Introduction Preview
Module 1: Overview of AI Ethics & Societal Impact
- 1.1 Introduction to Ethical Considerations in AI Preview
- 1.2 Understanding The Societal Impact of AI Technologies Preview
- 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 Preview
- 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 Preview
- 3.2 Techniques for Explaining AI Models to Diverse Stakeholders Preview
- 3.3 Guided Projects on Designing and Analysis of AI Systems with Ethical Considerations
Module 4: Privacy and Security Issues in AI
- Study frameworks for holding organizations accountable for the ethical use of AI.
- Why it matters: Ensures ethical AI deployment and helps mitigate the consequences of potential 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 Organizations
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 Organizational 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
- 1. Understanding AI Agents
- 2. Case Studies
- 3. Hands-On Practice with AI Agents
Tools you will 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
Lesmethode
Instructor-led OR Self-paced course + Official exam + Digital badge
Kenmerken
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Voorkennis
- Basic knowledge of artificial intelligence, machine learning concepts, Python familiarity, fundamental AI/ML concepts

