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Writer's pictureVishwanath Akuthota
May 272 min read

Explainability AI(XAI) beyond the Surface

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


XAI

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:

  1. SHAP - SHAP Documentation

  2. LIME - LIME Documentation

  3. ELI5 - ELI5 Documentation

  4. Alibi - https://docs.seldon.io/projects/alibi/en/latest/

  5. interpret - interpret Documentation

  6. Dalex - https://dalex.drwhy.ai/python/api/

  7. Captum - Captum Documentation

  8. Skater - https://github.com/GapData/skater

  9. Fairlearn - Fairlearn Documentation

  10. Fairness Indicators - https://github.com/tensorflow/fairness-indicators, https://notebook.community/tensorflow/fairness-indicators/fairness_indicators/documentation/examples/Fairness_Indicators_Example_Colab

  11. Yellowbrick - https://www.scikit-yb.org/en/latest/

  12. PyCEbox - PyCEbox Documentation

  13. Anchor - Anchor Documentation

  14. SHAPash - SHAPash Documentation

  15. DiCE - DiCE Documentation

  16. Aequitas - https://github.com/dssg/aequitas

  17. CleverHans - CleverHans Documentation

  18. PrivacyRaven - PrivacyRaven Documentation

  19. interpretML - interpretML Documentation

  20. PDPbox - PDPbox Documentation

  21. Fairness - Fairness Documentation

  22. FAT Forensics - FAT Forensics Documentation

  23. What-If Tool - https://pair-code.github.io/what-if-tool/

  24. certifai - certifai Documentation

  25. Explanatory Model Analysis - https://ema.drwhy.ai/

  26. XAI - XAI Documentation

  27. Fairness Comparison - Fairness Comparison Documentation

  28. AI Explainability 360 - https://aix360.readthedocs.io/en/latest/

  29. BlackBoxAuditing - BlackBoxAuditing Documentation

  30. Deap - Deap Documentation

  31. Facets - https://github.com/BCG-X-Official/facet

  32. TCAV - TCAV Documentation

  33. Grad-CAM - Grad-CAM Documentation

  34. AIX360 - AIX360 Documentation

  35. fairkit-learn - fairkit-learn Documentation

  36. Adversarial Robustness Toolbox (ART) - ART Documentation

  37. ExplainX.ai - ExplainX.ai Documentation

  38. Treeinterpreter - Treeinterpreter Documentation

  39. H2O.ai Explainability - H2O.ai Explainability Documentation

  40. TensorFlow Explain - https://www.tensorflow.org/guide -- https://tf-explain.readthedocs.io/en/latest/usage.html

  41. Concept Activation Vectors - Concept Activation Vectors Documentation

  42. Holoclean - Holoclean Documentation

  43. Saabas - https://cran.r-project.org/web/packages/tree.interpreter/vignettes/MDI.html

  44. RelEx - RelEx Documentation

  45. iNNvestigate - iNNvestigate Documentation

  46. Profweight - Profweight Documentation

  47. XDeep - XDeep Documentation

  48. DeepLIFT - DeepLIFT Documentation

  49. L2X - L2X Documentation

  50. Fiddler AI - Fiddler AI Documentation

  51. TrustyAI - TrustyAI Documentation

  52. RAI - RAI Documentation

  53. LimeTabular - LimeTabular Documentation

  54. Gamut - Gamut Documentation

  55. cxplain - cxplain Documentation

  56. AnchorTabular - AnchorTabular Documentation

  57. H2O-3 Explainability - https://docs.h2o.ai/h2o/latest-stable/h2o-docs/explain.html

  58. Alibi Detect - https://github.com/SeldonIO/alibi-detect

  59. WeightWatcher - WeightWatcher Documentation


These resources provide comprehensive guides and examples for implementing and understanding the respective tools and frameworks.


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