Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead - Cynthia Rudin

Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead - Cynthia Rudin

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Introduction

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1 of 32

Introduction

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Classroom Contents

Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead - Cynthia Rudin

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  1. 1 Introduction
  2. 2 Bad decisions
  3. 3 Definitions
  4. 4 Why
  5. 5 Article
  6. 6 Crossvalidation
  7. 7 Accuracy interpretability
  8. 8 Two types of machine learning problems
  9. 9 Critically ill patients
  10. 10 Two helps to be
  11. 11 Optimization problem
  12. 12 Saliency maps
  13. 13 Casebased reasoning
  14. 14 My network
  15. 15 Prototype layer
  16. 16 Redbellied woodpecker
  17. 17 Wilsons warbler
  18. 18 Accuracy vs interpretability
  19. 19 Computeraided mammography
  20. 20 Interpretable AI
  21. 21 Case Study
  22. 22 Results
  23. 23 Two Layer Additive Risk Model
  24. 24 Submission to Special Issue
  25. 25 Paper
  26. 26 Problems
  27. 27 Most powerful argument
  28. 28 Papers
  29. 29 Interactive tool
  30. 30 Demo
  31. 31 Optimization
  32. 32 Machine Learning in Medicine

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