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Starts 6 June 2025 13:08
Ends 6 June 2025
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Robust Algorithmic Recourse with Predictions
Explore algorithmic recourse in machine learning, focusing on creating robust suggestions that remain valid even when models change, using predictions to balance cost-effectiveness and reliability.
Simons Institute
via YouTube
Simons Institute
2484 Courses
46 minutes
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Overview
Explore algorithmic recourse in machine learning, focusing on creating robust suggestions that remain valid even when models change, using predictions to balance cost-effectiveness and reliability.
Syllabus
- Introduction to Algorithmic Recourse
- Fundamentals of Machine Learning Predictions
- Algorithmic Recourse Strategies
- Robustness in Algorithmic Recourse
- Balancing Cost-Effectiveness and Reliability
- Techniques for Robust Recourse
- Evaluating Recourse Outcomes
- Case Studies
- Ethical and Fairness Considerations
- Future Directions in Algorithmic Recourse
- Conclusion and Course Review
Definition and importance
Historical context and evolution
Overview of prediction models
Prediction accuracy and reliability
Types of recourse actions
Key factors influencing recourse effectiveness
Challenges with model drift and changes
Metrics for measuring robustness
Cost analysis of recourse actions
Designing cost-effective strategies
Sensitivity analysis
Model agnostic methods
Case-based and rule-based approaches
Metrics for success
Long-term monitoring and adaptation
Real-world applications and lessons learned
Comparative analysis of different approaches
Ensuring fairness in recourse
Addressing bias and discrimination
Emerging technologies
Research opportunities and challenges
Key takeaways
Discussion on future career and research paths in algorithmic recourse
Subjects
Computer Science
