Topics in Algorithmic Fairness

Overview

Explore the critical challenges of algorithmic fairness in AI. This course offers deep insights into the origins of bias and guides you through various fairness frameworks, emphasizing Demographic Parity.

It also examines the connection between fair classification and regression in automated decision-making systems, ensuring a well-rounded understanding of the complexities involved. Perfect for anyone looking to enhance their knowledge in artificial intelligence and computer science, provided by YouTube.

Syllabus

• Introduction to Algorithmic Fairness
Definition and Importance
Historical Context and Impact
• Origins of Bias in AI Systems
Data Collection and Representation Bias
Algorithmic Design Bias
Case Studies of Bias in AI
• Fairness Frameworks in AI
Overview of Fairness Metrics
Group Fairness vs. Individual Fairness
Tension Between Fairness and Accuracy
• Demographic Parity
Definition and Mathematical Formulation
Advantages and Limitations
Practical Applications
• Relationship Between Fair Classification and Regression
Fair Classification: Concepts and Techniques
Fair Regression: Approaches and Challenges
Comparing and Contrasting Fairness Measures
• Fairness in Automated Decision-Making Systems
Real-World Scenarios: Credit Scoring, Hiring, Criminal Justice
Designing Fair Decision-Making Systems
• Tools and Techniques for Ensuring Fairness
Pre-processing, In-processing, and Post-processing Methods
Bias Detection and Mitigation Tools
• Case Studies and Current Research
Recent Advances in Algorithmic Fairness
Emerging Challenges and Opportunities
• Ethical and Social Implications of Algorithmic Fairness
Fairness and Accountability
Regulatory and Policy Implications
• Conclusion and Future Directions in Algorithmic Fairness
Key Takeaways
Open Research Questions and Next Steps

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Subjects

Computer Science