SocialCounterfactuals: Probing and Mitigating Intersectional Social Bias with Counterfactuals

Overview

Explore how counterfactuals can be used to probe and mitigate intersectional social bias in AI systems, examining innovative techniques for creating more equitable technology.

Syllabus

• Introduction to Social Bias in AI
Understanding Social Bias and Its Impact
Intersectionality in AI Systems
Overview of Mitigating Techniques
• Introduction to Counterfactuals
Definition and Theory of Counterfactuals
Historical Context and Applications in AI
• Probing Social Bias with Counterfactuals
Techniques for Generating Counterfactuals
Identifying Bias Through Counterfactual Analysis
Case Studies: Real-World Applications
• Mitigating Intersectional Bias Using Counterfactuals
Designing Bias-Resistant AI Models
Counterfactual Fairness: Theory and Practice
Strategies for Implementing Counterfactual Mitigations
• Tools and Frameworks for Counterfactual Analysis
Overview of Popular Tools and Libraries
Hands-on Session: Using Counterfactual Tools
• Evaluation of Counterfactual Methodologies
Metrics for Success in Bias Mitigation
Challenges and Limitations in Current Approaches
Ethical Considerations in Counterfactual Usage
• Emerging Trends and Future Directions
Recent Innovations in Counterfactual Research
The Future of Equitable AI Systems
Ongoing Research and Community Efforts
• Conclusion and Course Recap
Key Takeaways on Counterfactuals and Bias
Discussion: Pathways to More Equitable Technology
• Capstone Project
Application of Course Concepts to a Real-World Problem
Presentation and Peer Review of Solutions

Subjects

Computer Science