AI+ Ethics Fundamentals™ eLearning Voeg toe aan favorieten

• 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

Doelgroep
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.

Doelstelling

• 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.

Programma
Course Overview

• Course Introduction Preview

Module 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

Module 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

Module 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

Module 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

Module 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

Module 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

Module 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

Module 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

Optional 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

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

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.

Studiewijze
De OCICT maatwerk-methode houdt in dat op basis van de behoefte van de klant - en indien nodig na een intakegesprek - een individueel opleidingstraject wordt opgesteld. De trainingen worden veelal individueel gevolgd, zodat iedere training een maatwerktraject is. Klassikale trainingen zijn natuurlijk mogelijk, mits ze aansluiten bij de leerbehoeften van de deelnemers / opdrachtgever.

Alle onderdelen van de training kennen een voortdurende wisselwerking tussen theorie en praktijk. De inhoud van de training kan vooraf per cursist worden aangepast en tijdens de cursus worden bijgesteld. De focus ligt op maatwerk: het traject wordt afgestemd op de doelstellingen van de deelnemer(s) en/of opdrachtgever, ongeacht of het individueel of klassikaal wordt aangeboden.