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Explore, Explain,
and Debug
Human-Centered Interpretable
Machine Learning
Przemysław Biecek
Model explanations
Why should we care?
https://www.massdevice.com/report-ibm-watson-delivered-unsafe-and-inaccurate-
cancer-recommendations/
• “You don’t see a lot of skepticism,” she says. “The algorithms are like shiny new
toys that we can’t resist using. We trust them so much that we project meaning on to
them.”
• Ultimately algorithms, according to O’Neil, reinforce discrimination and widen
inequality, “using people’s fear and trust of mathematics to prevent them from
asking questions”.
https://www.theguardian.com/books/2016/oct/27/cathy-oneil-weapons-of-math-
destruction-algorithms-big-data 6
Cathy O'Neil:
The era of blind faith
in big data must end
black boxes
Why do we need explanations for complex models?
http://drwhy.ai
8
How to explain
a complex model?
Use case
FIFA 2019
Source: https://www.kaggle.com/karangadiya/fifa19/data
Source: https://www.kaggle.com/karangadiya/fifa19/data
Source: https://www.kaggle.com/karangadiya/fifa19/data
library("gbm")
fifa_gbm <- gbm(Value~.,
data = fifa19,
n.trees = 250,
interaction.depth = 3)
Full name Ádám Csaba Szalai
Date of birth 9 December 1987
Place of birth Budapest, Hungary
Playing position Striker
wikipedia
Prediction from GBM model: 7 344 354 EUR
How? Why? Really?
Full name Ádám Csaba Szalai
Date of birth 9 December 1987
Place of birth Budapest, Hungary
Playing position Striker
wikipedia
Full name Péter Gulácsi
Date of birth 6 May 1990
Place of birth Budapest, Hungary
Playing position Goalkeeper
wikipedia
https://pbiecek.github.io/explainFIFA19/
Use case
Credit Scoring
https://buecker.ms/slides/crccxvi/slides.html#1
Performance for selected modeling methods
red models are the most interesting
https://buecker.ms/slides/crccxvi/slides.html#1
Champion vs. Challenger
Partial Dependency Plot for the most
important feature
https://buecker.ms/slides/crccxvi/slides.html#1
Single customer
https://buecker.ms/slides/crccxvi/slides.html#1
https://buecker.netlify.com/slides.html#34
https://buecker.ms/slides/crccxvi/slides.html#1
The most common
questions for XAI
https://kmichael08.github.io
XAI-BOT:
What would you ask for?
https://kmichael08.github.io
What If?
Why?
How good is
the prediction?
XAI-BOT:
What would you ask for?
When to use XAI?
Domain
understanding
Predictive
modeling
Validation and
Justification
EDA, transformations Linear models MSE, p-values
Shift in our focus: Statistics
Domain
understanding
Predictive
modeling
Validation and
Justification
Domain
understanding
Predictive
modeling
Validation and
Justification
EDA, transformations Linear models MSE, p-values
Simple EDA Lots of models + optimisation
Test/train
Cross Validation
Shift in our focus: Machine Learning
Domain
understanding
Predictive
modeling
Validation and
Justification
Domain
understanding
Predictive
modeling
Validation and
Justification
Domain
understanding
Predictive
modeling
Validation and
Justification
EDA, transformations Linear models MSE, p-values
Simple EDA
Test/train
Cross Validation
Simple EDA AutoML XAI, Fairness, Ethics
Validation and
Justification
Shift in our focus: Human Oriented ML?
Lots of models + optimisation
MDP : : Model Development Process
Data validation
Feature selection
Parameters tuning
Problem formulation Crisp modelling Fine tuning Maintaining
Data acquisition
Model deployment
Data cleaning
Data exploration
Sample selection
Feature engineering
Model selection
Model validation
Documentation
Communication
Data preparation
Data understanding
Model delivery
Model assembly
Model audit
Model benchmarking
Iterations P1 C1 C2 F1 F2 M1 M2 M3
time
MDP - process for model development
MDP : : Model Development Process
Data validation
Feature selection
Parameters tuning
Problem formulation Crisp modelling Fine tuning Maintaining
Data acquisition
Model deployment
Data cleaning
Data exploration
Sample selection
Feature engineering
Model selection
Model validation
Documentation
Communication
Data preparation
Data understanding
Model delivery
Model assembly
Model audit
Model benchmarking
Iterations P1 C1 C2 F1 F2 M1 M2 M3
time
MDP - process for model development
MDP : : Model Development Process
Data validation
Feature selection
Parameters tuning
Problem formulation Crisp modelling Fine tuning Maintaining
Data acquisition
Model deployment
Data cleaning
Data exploration
Sample selection
Feature engineering
Model selection
Model validation
Documentation
Communication
Data preparation
Data understanding
Model delivery
Model assembly
Model audit
Model benchmarking
Iterations P1 C1 C2 F1 F2 M1 M2 M3
time
MDP - process for model development
MDP : : Model Development Process
Data validation
Feature selection
Parameters tuning
Problem formulation Crisp modelling Fine tuning Maintaining
Data acquisition
Model deployment
Data cleaning
Data exploration
Sample selection
Feature engineering
Model selection
Model validation
Documentation
Communication
Data preparation
Data understanding
Model delivery
Model assembly
Model audit
Model benchmarking
Iterations P1 C1 C2 F1 F2 M1 M2 M3
time
MDP - process for model development
MDP : : Model Development Process
Data validation
Feature selection
Parameters tuning
Problem formulation Crisp modelling Fine tuning Maintaining
Data acquisition
Model deployment
Data cleaning
Data exploration
Sample selection
Feature engineering
Model selection
Model validation
Documentation
Communication
Data preparation
Data understanding
Model delivery
Model assembly
Model audit
Model benchmarking
Iterations P1 C1 C2 F1 F2 M1 M2 M3
time
pre-modelling

explore
in-modelling

explain
post-modelling

debug
pre-, in-, post- modelling explainability
Model specific / agnostic
explainability
Model specific Model agnostic
+ Exploits the structure of a model
+ Allow for deep diagnostic
- Explanations cannot be easily
compared between models
- You need to create an explainer
for every possible model
structure
Examples:
• randomForestExplainer
• EIX - lightgbm/xgboost explainer
+ Explanations can be compared
between different models
+ Can be used to any model
(model is a black box)
- Explanations are (im most
cases) approximations, they
may be inaccurate or wrong
- It is easy to miss some quirks
Examples:
LIME / SHAP / Break Down /
Partial Dependency Profiles /
Permutational Feature Importance
Local / global
explainability
Local explanations Global explanations
+ Focused on a single observation
+ Good for dissecting of a single
prediction
+ Good for debugging
- Feature effects may be different
for different observations
- Tricky for correlated features
Examples:
• LIME / SHAP / Break Down
• Ceteris Paribus
+ Focused on a model
+ Good for global model
summaries
+ Good for comparisons
- Local behaviour may be different
- For non-additive models they
may be to simplistic
Examples:
• Feature Importance
• Partial Dependency Profiles
An Introduction to Machine Learning Interpretability
Navdeep Gill, Patrick Hall

https://www.h2o.ai/oreilly-mli-booklet-2019/

Interpretable Machine Learning
Christoph Molnar

https://christophm.github.io/interpretable-ml-book/

Predictive Models: Explore, Explain, and Debug
Przemyslaw Biecek and Tomasz Burzykowski

https://pbiecek.github.io/PM_VEE/
https://github.com/ModelOriented/DALEX
Make models
not wars
model explainer
https://medium.com/@ModelOriented/xai-in-the-jungle-of-competing-frameworks-for-
machine-learning-fa6e96a99644
DALEX wraps a model available in R or python or through REST API into
a standardised container.
Other tools for XAI may then work on the model despite its internal structure.
https://medium.com/@ModelOriented/xai-in-the-jungle-of-competing-frameworks-for-
machine-learning-fa6e96a99644
Thank you!
Human-Centered Interpretable  Machine Learning

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Human-Centered Interpretable Machine Learning