In this post together we delve into Advanced XAI, XAI characteristics, Categories and Python libraries 📚
Characteristics of XAI:
Transparency
Justification
Informativeness
Reliability
Categories of XAI:
Feature attribution: Determine Feature Importance
Instance based: Determine Subset of features that guarantee a prediction
Graph Convolution based: Interpret Model using subgraphs
Self explaining: Develop Model that are explainable by Design
Uncertainty Estimation: Quantify the reliability of a prediction
Advanced XAI methods:
Diving into some examples in the categories and also other types:
Counterfactual: alternative scenarios that could have led to a different AI decision.
Human-Readable Rule Extraction: extract human-readable rules from complex models
Attention Mechanisms: highlight the important features or regions in input data that influenced an AI decision.
NLP techniques: can generate explanations in human-readable language to provide intuitive insights
Bayesian Networks: enable understanding of causal relationships within AI models.
Model Confidence: conveying model confidence and uncertainty in predictions.
Adversarial Explanation: testing AI models with intentionally modified inputs to reveal vulnerabilities or biases.
Transfer Learning: widely used for improving AI performance, can also enhance explainability.
Interactive Explanations: allow users to actively engage with AI systems and explore decision pathways.
Integrating Human Feedback Loops: by incorporating iterative feedback loops from human users.
Here are 60 Python Libraries for AI Explainability and Model Interpretability:
SHAP - SHAP Documentation
LIME - LIME Documentation
ELI5 - ELI5 Documentation
interpret - interpret Documentation
Captum - Captum Documentation
Skater - https://github.com/GapData/skater
Fairlearn - Fairlearn Documentation
Fairness Indicators - https://github.com/tensorflow/fairness-indicators, https://notebook.community/tensorflow/fairness-indicators/fairness_indicators/documentation/examples/Fairness_Indicators_Example_Colab
Yellowbrick - https://www.scikit-yb.org/en/latest/
PyCEbox - PyCEbox Documentation
Anchor - Anchor Documentation
SHAPash - SHAPash Documentation
DiCE - DiCE Documentation
Aequitas - https://github.com/dssg/aequitas
CleverHans - CleverHans Documentation
PrivacyRaven - PrivacyRaven Documentation
interpretML - interpretML Documentation
PDPbox - PDPbox Documentation
Fairness - Fairness Documentation
FAT Forensics - FAT Forensics Documentation
What-If Tool - https://pair-code.github.io/what-if-tool/
certifai - certifai Documentation
Explanatory Model Analysis - https://ema.drwhy.ai/
XAI - XAI Documentation
Fairness Comparison - Fairness Comparison Documentation
AI Explainability 360 - https://aix360.readthedocs.io/en/latest/
BlackBoxAuditing - BlackBoxAuditing Documentation
Deap - Deap Documentation
TCAV - TCAV Documentation
Grad-CAM - Grad-CAM Documentation
AIX360 - AIX360 Documentation
fairkit-learn - fairkit-learn Documentation
Adversarial Robustness Toolbox (ART) - ART Documentation
Treeinterpreter - Treeinterpreter Documentation
TensorFlow Explain - https://www.tensorflow.org/guide -- https://tf-explain.readthedocs.io/en/latest/usage.html
Concept Activation Vectors - Concept Activation Vectors Documentation
Holoclean - Holoclean Documentation
Saabas - https://cran.r-project.org/web/packages/tree.interpreter/vignettes/MDI.html
RelEx - RelEx Documentation
iNNvestigate - iNNvestigate Documentation
Profweight - Profweight Documentation
XDeep - XDeep Documentation
DeepLIFT - DeepLIFT Documentation
L2X - L2X Documentation
Fiddler AI - Fiddler AI Documentation
TrustyAI - TrustyAI Documentation
RAI - RAI Documentation
LimeTabular - LimeTabular Documentation
Gamut - Gamut Documentation
cxplain - cxplain Documentation
AnchorTabular - AnchorTabular Documentation
H2O-3 Explainability - https://docs.h2o.ai/h2o/latest-stable/h2o-docs/explain.html
Alibi Detect - https://github.com/SeldonIO/alibi-detect
WeightWatcher - WeightWatcher Documentation
These resources provide comprehensive guides and examples for implementing and understanding the respective tools and frameworks.
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