Robust Algorithmic Recourse with Predictions

Overview

Syllabus

• Introduction to Algorithmic Recourse
Definition and importance
Historical context and evolution
• Fundamentals of Machine Learning Predictions
Overview of prediction models
Prediction accuracy and reliability
• Algorithmic Recourse Strategies
Types of recourse actions
Key factors influencing recourse effectiveness
• Robustness in Algorithmic Recourse
Challenges with model drift and changes
Metrics for measuring robustness
• Balancing Cost-Effectiveness and Reliability
Cost analysis of recourse actions
Designing cost-effective strategies
• Techniques for Robust Recourse
Sensitivity analysis
Model agnostic methods
Case-based and rule-based approaches
• Evaluating Recourse Outcomes
Metrics for success
Long-term monitoring and adaptation
• Case Studies
Real-world applications and lessons learned
Comparative analysis of different approaches
• Ethical and Fairness Considerations
Ensuring fairness in recourse
Addressing bias and discrimination
• Future Directions in Algorithmic Recourse
Emerging technologies
Research opportunities and challenges
• Conclusion and Course Review
Key takeaways
Discussion on future career and research paths in algorithmic recourse

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Subjects

Computer Science