A Unified Framework for Model Explanation
Ian Covert, University of Washington
Explainable AI is becoming increasingly important, but the field is evolving rapidly and requires better organizing principles to remain manageable for researchers and practitioners. In this talk, Ian will discuss a new paper that unifies a large portion of the literature using a simple idea: simulating feature removal. The new class of "removal-based explanations" describes 20+ existing methods (e.g., LIME, SHAP) and reveals underlying links with psychology, game theory and information theory.
Practical examples will be presented and available on the Qu.Academy site
Reference:
Explaining by Removing: A Unified Framework for Model Explanation
Ian Covert, Scott Lundberg, Su-In Lee
https://arxiv.org/abs/2011.14878
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Explainable AI Workshop
1. Qu Speaker Series
Explainable AI Workshop
Explaining by Removing: A Unified Framework for Model Explanation
Ian Covert
University of Washington
2020 Copyright QuantUniversity LLC.
Hosted By:
Sri Krishnamurthy, CFA, CAP
sri@quantuniversity.com
www.qu.academy
12/16/2020
Qu.Academy
https://quspeakerseries18.splashthat.com/
2. 2
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13. > Researchers have made significant progress
> The field is fragmented
> Growing very fast
> Lacking discussion of underlying principles
Is the field in a good place?
15. 1. Motivation
2. A unified framework
3. Demonstration
4. Why feature removal?
5. Are there “right” choices?
Contents
16. > Many methods implicitly simulate feature removal
> Non-trivial operation, different approaches
> Further differences in generating final “explanation”
Explaining by removing
21. > The model 𝒇 requires a specific set of features
𝒇(𝒙) for 𝒙 ∈ 𝓧
> Require a subset function 𝑭 that accepts a feature subset
𝑭(𝒙 𝑺) for 𝒙 ∈ 𝒳 and 𝑺 ⊆ 𝟏, 𝟐, … , 𝒅
1. Feature removal
32. > Removal: default values
> Behavior: individual prediction
> Summary: fit linear model
Example: LIME (2016)
33. > Removal: marginalize out
> Behavior: individual prediction (same as LIME)
> Summary: Shapley value
Example: SHAP (2017)
34. > Removal: marginalize out (same as SHAP)
> Behavior: dataset loss
> Summary: Shapley value (same as SHAP)
Example: SAGE (2020)
35. > Removal: marginalize out (same as SAGE)
> Behavior: dataset loss (same as SAGE)
> Summary: remove individual
Example: permutation test (2001)
−
36. > 20+ existing methods
> Local and global
> Feature attribution and
feature selection
A unifying framework
37. A unifying framework
Remove
individual
Include
individual
Mean when
included
Shapley value
Linear
model
High value
subset
Low value
subset
Partitioned
subsets
Zeros
Occlusion
CXPlain
RISE MM
Default values
LIME
(images)
Extend pixels MIR
Blurring EP MP
Generative
model
FIDO-CA
Marginalize
(replacement
distribution)
LIME
(tabular)
Marginalize
(uniform)
IME (2010)
Marginalize
(marginals
product)
QII
Marginalize
(marginal)
Permutation
test
SHAP
KernelSHAP
Marginalize
(conditional)
PredDiff
Conditional
perm. test
SHAP SAGE
LossSHAP
Shapley Effects
Tree
distribution
TreeSHAP
Missingness
during training
L2X
INVASE
Separate
models
Feature
ablation
Univariate
predictors
IME (2009)
Shapley Net
Effects
Summary technique
Featureremoval
∎ Prediction ∎ Prediction loss ∎ Mean prediction loss ∎ Dataset loss ∎ Dataset loss (output)Model behavior
Feature attribution Feature selection
> Method “space”
> Neighboring methods
> Unique methods, new
methods
38. 1. Motivation
2. A unified framework
3. Demonstration
4. Why feature removal?
5. Are there “right” choices?
Contents
40. Summary
> A new class of methods based on feature removal
> Each method is specified by three choices
> Framework offers a great degree of flexibility
Removal-based explanations
41. 1. Is feature removal a smart approach to model explanation?
2. Are there “right” choices for each dimension?
Key questions
42. 1. Motivation
2. A unified framework
3. Demonstration
4. Why feature removal?
5. Are there “right” choices?
Contents
43. > Intuitive to many research groups
> Feature removal is a form of counterfactual reasoning
> Undo act of observing information (rather than changing what was
observed)
> Removal is anchored in psychology (subtractive counterfactual) and
philosophy (method of difference)
Why feature removal?
44. > Counterfactuals change aspects of a situation
(observation of feature values)
> Can understand models by changing inputs
> Often complicated to explore
> Feature removal gives a more practical way to
explore and summarize functions
Counterfactual reasoning
45. 1. Motivation
2. A unified framework
3. Demonstration
4. Why feature removal?
5. Are there “right” choices?
Contents
46. “Right” choices?
> Methods determined by three choices
> Every method has something to offer
> Conceptual and computational trade-offs
47. > Marginalizing out features with their conditional
distribution
> Difficult to implement, but many approximations
> Yields information-theoretic explanations
Feature removal strategy
48. > Intuitively (Chest X-Ray)
> How should a doctor interpret this?
> Mathematically:
𝑭 𝒙 𝑺 = 𝔼 𝒇 𝑿 𝑿 𝑺 = 𝒙 𝑺
= ∫ 𝒑 𝒙(𝑺 𝒙 𝑺 𝒇(𝒙 𝑺, 𝒙(𝑺)
Conditional distribution removal
53. 1. Motivation
2. A unified framework
3. Why feature removal?
4. Demonstration
5. Are there “right” choices?
6. Conclusion + questions
Contents
54. > Presented a new perspective for understanding explainability tools
> Developed rigorous foundations
> Aim to inform practitioners, guide researchers
Concluding thoughts
56. 56
Instructions for the Lab:
1. Go to https://academy.qusandbox.com/#/register and register using the code:
"QUFALLSCHOOL"
57. Thank you!
Sri Krishnamurthy, CFA, CAP
Founder and CEO
QuantUniversity LLC.
srikrishnamurthy
www.QuantUniversity.com
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