Raden Muhammad Hadi - Interpretable machine learning & fairness
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My talks about Interpretable Machine Learning & Fairness @ DevFest Bandung 2019
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Raden Muhammad Hadi - Interpretable machine learning & fairness
- 2. Interpretable Machine Learning & Fairness Raden Muhammad Hadi
- 3. Raden Muhammad Hadi Quantus Telematika Indonesia Mathematical Modeler
- 4. Outline ● What happening now? ● What is interpretability and why do we need it? ● How to interpret (a) model(s)? ● Why cares about it? ● What tools I can use? ● References ● Takeaways
- 12. Scientific?
- 13. Scientific?
- 14. Scientific?
- 19. Deep Learning?
- 20. The Ugly Truths
- 21. AI In Hiring Process
- 25. AI In Credit Scoring
- 29. AI In Criminal Justice
- 32. Overall, Northpointe’s assessment tool correctly predicts recidivism 61 percent of the time. But blacks are almost twice as likely as whites to be labeled a higher risk but not actually re-offend. It makes the opposite mistake among whites: They are much more likely than blacks to be labeled lower risk but go on to commit other crimes. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
- 36. Quick Quiz
- 46. Pointy ears
- 51. CAT 80%
- 52. Random noise
- 55. OVARIAN CANCER “We need to remove your wombs”
- 56. Imagine
- 59. “Interpretability is the degree to which a human can understand the cause of a decision.” Tim Milner on “Explanation in Artificial Intelligence: Insights from the Social Sciences” https://arxiv.org/abs/1706.07269
- 62. Why do we need interpretability?
- 63. 5 Reasons for Interpretability Compliance to legislation Verify that classifier works as expected Interpretability in science Learn from the learning machine Improve Classifier
- 64. How to interpret (a) model(s)?
- 71. Why Cares?
- 78. Problems
- 82. Interpretability - Accuracy Tradeoff
- 84. What tools I can use
- 85. IML package for R Features included: ● Feature importance ● Partial dependence plots ● Individual conditional expectation plots (ICE) ● Accumulated local effects ● Tree surrogate ● LocalModel: Local Interpretable Model-agnostic Explanations ● Shapley value for explaining single predictions https://github.com/christophM/iml
- 88. DrWhy.AI Model Development Framework for R https://github.com/ModelOriented/DrWhy
- 90. Fairness R Package Metrics included: ● Demographic Parity ● Proportional Parity ● Equalized Odds ● Predictive Rate Parity ● Accuracy Parity ● Visualizations https://cran.r-project.org/web/packages/fairness/index.html
- 91. Yellowbrick Python Package Features included: ● Feature Visualization ● Classification Visualization ● Model Selection Visualization ● Target Visualization ● Text Visualization https://www.scikit-yb.org/en/latest/
- 93. Skater Python Package Feature includes: ● Global Interpretation ○ Feature Importance ○ Partial Dependence Plot ● Local Interpretation ○ LIME ○ Layer-Wise Relevance Propagation ○ Integrated Gradient ● Hybrid ○ Scalable Bayesian Rule List ○ Tree Surrogateshttps://oracle.github.io/Skater/overview.html
- 94. ELI5 Python Package Features included: ● Explainer for model built by ○ Scikit-Learn ○ Keras ○ XGBoost ○ LightGBM ○ CatBoost ○ Sklearn-CRFSuite ● Permutation Importance ● Text Explainer https://eli5.readthedocs.io/en/latest/
- 95. Aequitas Python Package for Model Auditing Metrics Included: ● Predicted Positive ● Total Predictive Positive ● Predicted Prevalence ● False Discovery Rate ● False Omission Rate ● Visualizationshttp://www.datasciencepublicpolicy.org/projects/aequitas/
- 98. Takeaways Understand your model Audit your model
- 99. Takeaways You’re not only responsible to increase your company ROI, think about people/group/gender who get impacted by your model
- 100. Thank You and enjoy the event!