Overview
Explore the ethical implications of machine learning, examining bias in algorithms and its real-world consequences. Learn about cutting-edge research on counteracting bias and consider unanswered questions in AI morality.
Syllabus
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- Introduction to Ethics in AI
-- Overview of AI Ethics
-- Importance of Ethical Considerations in Machine Learning
- Understanding Bias in Algorithms
-- Types of Bias in Machine Learning
-- Sources of Bias in Data and Algorithms
- Real-World Consequences of Algorithmic Bias
-- Case Studies of Bias in AI Systems
-- Societal Impact of Biased Algorithms
-- Discussion on Accountability and Transparency in AI
- Techniques for Identifying Bias in Algorithms
-- Methods for Bias Detection
-- Tools and Frameworks for Measuring Bias
- Counteracting Bias in Machine Learning
-- Strategies for Mitigating Bias
-- Ethical AI Design Principles
-- Fairness-Aware Machine Learning Techniques
- Cutting-Edge Research in AI Ethics
-- Recent Advances and Developments
-- Innovative Solutions in Bias Reduction
- The Future of AI Morality
-- Unanswered Questions in AI Ethics
-- The Role of Policy and Regulation
-- AI and Human Oversight
- Case Study Project
-- Select a real-world AI application
-- Analyze potential biases and ethical concerns
-- Propose solutions for ethical improvement
- Conclusion and Future Directions
-- Recap of Key Ethical Considerations
-- Discussion on Future Trends in AI and Ethics
- Course Wrap-up
-- Final Reflections
-- Further Reading and Resources
Taught by
Tags
On-Demand
