Responsible AI: A Non-Technical Guide for Managers

Übersicht

Balancing AI Innovation with Ethics:

Fairness, Privacy, and Generative AI Risks for Managers What you'll learn:

Understand the main risks of working with (generative) AI, related to:

privacy, fairness, explainability, transparency and accountability.Evaluate AI projects in terms of responsible AIUnderstand these risks and responsible AI practices through real-world examples in areas as banking, recruitment, healthcare and education.Understand main issues related to regulations, such as the EU AI ActDefine the role and responsibilities of a Responsible AI BoardImplement Strategies for AI Risk MitigationPromote Organizational AI LiteracyUnderstand and Plan for Long-Term AI Risks In today's rapidly evolving technological landscape, understanding and managing the risks associated with AI is crucial for business leaders.Course Overview:

"Responsible AI:

A Non-Technical Guide for Managers" povides a high-level introduction and is tailored to equip managers with the knowledge and tools necessary to navigate the complexities of AI implementation responsibly. The course provides both key concepts, some initial solutions and various cautionary tales to make the issue more tangible.The course is structured in five Sections:

Intro to Machine Learning, AI and Responsible AICore Principles of Responsible AI related to privacy, fairness and transparencyGenerative AI Risks as hallucinations, data leakage and biasAdvanced Topics in Responsible AI related to sustainability,AGI and ASIImplementing Responsible AI in Practice focusing on the responsible AI board, principles and trainingWhy Enroll?This course offers a comprehensive, non-technical approach, making it accessible to managers from diverse backgrounds.Through real-world examples and practical insights, you'll gain the confidence to lead AI initiatives that are not only innovative but also ethically sound and sustainable.Whether you're new to AI or looking to deepen your understanding of its ethical implications, this course equips you to lead responsibly in a rapidly evolving technological landscape.

Lehrplan

• **Introduction to Machine Learning, AI, and Responsible AI**
Overview of AI and Machine Learning
Definition and Importance of Responsible AI
Case Studies: Successful and Unsuccessful AI Implementations
• **Core Principles of Responsible AI**
Privacy: Data Protection and User Consent
Fairness: Avoiding Discrimination and Bias
Transparency: Explainability and Interpretability of AI Models
Accountability: Responsibility in AI Decision-Making
• **Generative AI Risks**
Understanding Hallucinations in Generative Models
Risks of Data Leakage and Privacy Breaches
Identifying and Mitigating Bias in Generative AI
• **Advanced Topics in Responsible AI**
AI Sustainability and Environmental Impact
Long-term Considerations with AGI and ASI (Artificial General and Super Intelligence)
The Role of AI in Shaping Future Regulatory Landscapes
• **Implementing Responsible AI in Practice**
Establishing a Responsible AI Board: Roles and Responsibilities
Developing and Enforcing Ethical AI Principles
Training and Promoting AI Literacy within Organizations
Strategies for AI Risk Mitigation
Planning for Long-Term AI Risks
• **Regulatory Frameworks and Guidelines**
Overview of the EU AI Act and its Implications
Understanding Global Regulatory Trends and Compliance
• **Concluding Insights and Strategic Vision**
Real-World Examples: AI in Banking, Recruitment, Healthcare, and Education
Building a Culture of Ethical AI Leadership
Strategies for Continuous Learning and Adaptation in AI
• **Course Wrap-Up**
Summary of Key Learnings
Final Discussion and Q&A

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Unterrichtet von

David Martens

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Fachgebiete

Computer Science