Fairness and Robustness in Machine Learning – A Formal Methods Perspective - Aditya Nori, Microsoft

Fairness and Robustness in Machine Learning – A Formal Methods Perspective - Aditya Nori, Microsoft

Alan Turing Institute via YouTube Direct link

Challenges with sampling

13 of 18

13 of 18

Challenges with sampling

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Fairness and Robustness in Machine Learning – A Formal Methods Perspective - Aditya Nori, Microsoft

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  1. 1 Introduction
  2. 2 New programming language challenges
  3. 3 How does one formalize the notion of fairness
  4. 4 Algorithmic decision making
  5. 5 Questions
  6. 6 Question
  7. 7 Population model
  8. 8 Symbolic execution
  9. 9 Invariants
  10. 10 Triangle example
  11. 11 Hyper rectangular decomposition
  12. 12 Subsampling hyper rectangles
  13. 13 Challenges with sampling
  14. 14 Ideal solution
  15. 15 Approximate density
  16. 16 Properties
  17. 17 Proofs
  18. 18 Summary

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