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

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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