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Recent advances in
explainable machine
learning research
Bernease Herman, Research Scientist
University of Washington eSci...
Recent advances in
explainable machine
learning research
Bernease Herman, Research Scientist
University of Washington eSci...
eScience Institute
University of Washington
Modified graphic. Original from Alice Zheng
Model
interpretation
Data Curation · Evaluation · Interpretation
Personal rese...
Modified graphic. Original from Alice Zheng
Model
interpretation
Data Curation · Evaluation · Interpretation
How do we cho...
Modified graphic. Original from Alice Zheng
Model
interpretation
Data Curation · Evaluation · Interpretation
How to check ...
Modified graphic. Original from Alice Zheng
Model
interpretation
Data Curation · Evaluation · Interpretation
What’s happen...
Interpretable vs Explainable
Defining model explainability
No single definition within community.
“component of interpretable modeling process informing
...
Linear regression models
(with a certain number of parameters)
Decision trees (or similar)
(with a certain depth/number of...
“Explanation vehicles” (Herman 2017)
Explanations come in many forms
from “The Mythos of Model Interpretability”, Lipton 2016
1. Simulatability, comprehend the entire model
at once; model com...
Interpretability methods
(with focus on explanations)
Interpretable methods survey
heavily borrowed from Kim & Doshi-Valez ICML 2017 tutorial
1. Fitting new models that are
int...
Inherently interpretable models
1. Fitting new models that are
intrinsically interpretable
Decision trees, rule lists, rul...
1. Fitting new models that are
intrinsically interpretable.
Decision trees, rule lists, rule sets
Generalized linear model...
figure above from Gupta et al. 2016
Monotonicity constraints
Conceptual and hierarchical models
Inherently interpretable mo...
2. Post-hoc analysis of existing model
Sensitivity analysis
Surrogate models
Gradient-based methods
Hidden layer investiga...
2.
Sensitivity analysis
Surrogate models
Patrick Hall et al. 2017, O’Reilly Blog
Post-hoc for existing models
2. Post-hoc analysis of existing model.
Sensitivity analysis
Surrogate models
Gradient-based methods
Surrogate models
Hidd...
2. Post-hoc analysis of existing mode
Gradient-based methods
Surrogate models
Hidden layer investigations
Post-hoc for exi...
2. Post-hoc analysis of existing model
Gradient-based methods
Surrogate models
Hidden layer investigations
Post-hoc for ex...
2.
Sensitivity analysis
Surrogate models
Gradient-based methods
Selvaraju et al. 2017
Post-hoc for existing models
3. Interpretable analysis of raw data
Visualization
Variable Importance
Partial dependence plots
Correlation analysis
Inte...
Visualizations for AutoML
Wang et al. (2019)
Do interpretability and
explainability methods
always work?
Explanations can be persuasive
Lipton 2016, Herman 2017
When tailoring our model explanations to
human preferences and jud...
Potentially unfaithful explanations
Hendricks et al. 2016
Visual saliency explanation methods
Adebayo et al. 2018
Even random labels don’t stop them
Adebayo et al. 2018
How AI detectives are cracking open the black box of
deep learning, Science Magazine, July 2017
Background reading
Ideas on interpreting machine learning,
O’Reilly Ideas, March 2017
Background reading
Thank you!
Let’s discuss this more.
Bernease Herman
bernease@uw.edu
@bernease on Twitter, Github, MSDSE Slack, everything
Splitting model form from simplicity
Herman 2017
Simultaneously
coerced into suitable
model form
(e.g., decision tree)
and...
Splitting model form from simplicity
Herman 2017
Keeps model form
and reduction of
complexity separate.
Improves evaluatio...
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Rsqrd AI: Recent Advances in Explainable Machine Learning Research

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In this talk, Bernease Herman speaks about recent explainable ML research

Presented on 06/06/2019

**These slides are from a talk given at Rsqrd AI. Learn more at rsqrdai.org**

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Rsqrd AI: Recent Advances in Explainable Machine Learning Research

  1. 1. Recent advances in explainable machine learning research Bernease Herman, Research Scientist University of Washington eScience Institute and Paul G. Allen School of Computer Science & Engineering June 6, 2019 - Allen Institute for Artificial Intelligence
  2. 2. Recent advances in explainable machine learning research Bernease Herman, Research Scientist University of Washington eScience Institute and Paul G. Allen School of Computer Science & Engineering June 6, 2019 - Allen Institute for Artificial Intelligence Speed run edition
  3. 3. eScience Institute University of Washington
  4. 4. Modified graphic. Original from Alice Zheng Model interpretation Data Curation · Evaluation · Interpretation Personal research interests in ML
  5. 5. Modified graphic. Original from Alice Zheng Model interpretation Data Curation · Evaluation · Interpretation How do we choose and collect dataset?
  6. 6. Modified graphic. Original from Alice Zheng Model interpretation Data Curation · Evaluation · Interpretation How to check performance of ML?
  7. 7. Modified graphic. Original from Alice Zheng Model interpretation Data Curation · Evaluation · Interpretation What’s happening on the inside?
  8. 8. Interpretable vs Explainable
  9. 9. Defining model explainability No single definition within community. “component of interpretable modeling process informing how model works in understandable form” - Me “process of giving explanations [of ML] to humans” - Kim & Doshi-Valez 2017 to left, formal definition (I) that extends beyond humans - Dhurandhar et al. 2017
  10. 10. Linear regression models (with a certain number of parameters) Decision trees (or similar) (with a certain depth/number of parameters) Text explanations Visualizations (e.g., saliency maps) more Explanations come in many forms
  11. 11. “Explanation vehicles” (Herman 2017) Explanations come in many forms
  12. 12. from “The Mythos of Model Interpretability”, Lipton 2016 1. Simulatability, comprehend the entire model at once; model complexity 2. Decomposability, comprehend the individual components/parameters; intelligibility [2] 3. Algorithmic transparency, comprehend the algorithm behavior; loss surface and randomness Three levels of transparency
  13. 13. Interpretability methods (with focus on explanations)
  14. 14. Interpretable methods survey heavily borrowed from Kim & Doshi-Valez ICML 2017 tutorial 1. Fitting new models that are intrinsically interpretable 2. Post-hoc analysis of existing model 3. Interpretable analysis of raw data (or model architecture)
  15. 15. Inherently interpretable models 1. Fitting new models that are intrinsically interpretable Decision trees, rule lists, rule sets Generalized linear models (and feature manipulation) Case-based methods Sparsity-based methods Monotonicity-based methods Conceptual and hierarchical models
  16. 16. 1. Fitting new models that are intrinsically interpretable. Decision trees, rule lists, rule sets Generalized linear models (and feature manipulation) table above from Gehrke et al. 2012 Inherently interpretable models
  17. 17. figure above from Gupta et al. 2016 Monotonicity constraints Conceptual and hierarchical models Inherently interpretable models
  18. 18. 2. Post-hoc analysis of existing model Sensitivity analysis Surrogate models Gradient-based methods Hidden layer investigations Post-hoc for existing models
  19. 19. 2. Sensitivity analysis Surrogate models Patrick Hall et al. 2017, O’Reilly Blog Post-hoc for existing models
  20. 20. 2. Post-hoc analysis of existing model. Sensitivity analysis Surrogate models Gradient-based methods Surrogate models Hidden layer investigations Post-hoc for existing models figures from Ribeiro et al. 2015 (LIME)
  21. 21. 2. Post-hoc analysis of existing mode Gradient-based methods Surrogate models Hidden layer investigations Post-hoc for existing models Lundberg & Lee 2017 (SHAP)
  22. 22. 2. Post-hoc analysis of existing model Gradient-based methods Surrogate models Hidden layer investigations Post-hoc for existing models Lundberg et al. (2019)
  23. 23. 2. Sensitivity analysis Surrogate models Gradient-based methods Selvaraju et al. 2017 Post-hoc for existing models
  24. 24. 3. Interpretable analysis of raw data Visualization Variable Importance Partial dependence plots Correlation analysis Interpretable raw data analysis
  25. 25. Visualizations for AutoML Wang et al. (2019)
  26. 26. Do interpretability and explainability methods always work?
  27. 27. Explanations can be persuasive Lipton 2016, Herman 2017 When tailoring our model explanations to human preferences and judges, our models may learn to prioritize persuasive explanations over introspective ones.
  28. 28. Potentially unfaithful explanations Hendricks et al. 2016
  29. 29. Visual saliency explanation methods Adebayo et al. 2018
  30. 30. Even random labels don’t stop them Adebayo et al. 2018
  31. 31. How AI detectives are cracking open the black box of deep learning, Science Magazine, July 2017 Background reading
  32. 32. Ideas on interpreting machine learning, O’Reilly Ideas, March 2017 Background reading
  33. 33. Thank you! Let’s discuss this more. Bernease Herman bernease@uw.edu @bernease on Twitter, Github, MSDSE Slack, everything
  34. 34. Splitting model form from simplicity Herman 2017 Simultaneously coerced into suitable model form (e.g., decision tree) and reduced in complexity (e.g., model size). Difficult to evaluate across complexity preferences.
  35. 35. Splitting model form from simplicity Herman 2017 Keeps model form and reduction of complexity separate. Improves evaluation and adaptability.

In this talk, Bernease Herman speaks about recent explainable ML research Presented on 06/06/2019 **These slides are from a talk given at Rsqrd AI. Learn more at rsqrdai.org**

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