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Explainable AI (XAI) and Model Interpretability

Understanding Explainable AI (XAI) is pivotal as AI models grow in complexity, ensuring decisions are transparent, trustworthy, and verifiable. The chapter emphasizes the significance of model interpretability, explores various methods such as LIME and SHAP, and highlights the ethical and regulatory implications in fields like finance and healthcare. The interplay between model accuracy and interpretability is critical for responsible AI deployment.

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Sections

  • 1

    What Is Explainable Ai (Xai)?

    XAI encompasses methods to make AI models' decision-making processes more transparent.

    Learning Practice

  • 2

    Types Of Model Interpretability

    This section outlines the various types of model interpretability, including global and local interpretability, intrinsic and post-hoc explanations.

    Learning Practice

  • 2.1

    Global

    This section introduces Explainable AI (XAI) and emphasizes the importance of understanding AI model decisions.

    Learning Practice

  • 2.2

    Local

    This section emphasizes the importance of local interpretability in AI models, explaining how specific predictions can be understood and trusted.

    Learning Practice

  • 2.3

    Intrinsic

    Intrinsic interpretability involves understanding model behavior through inherent characteristics, often seen in simpler models like decision trees or linear regression.

    Learning Practice

  • 2.4

    Post-Hoc

    Post-hoc interpretability methods help explain AI model decisions after the model has been trained.

    Learning Practice

  • 3

    Popular Xai Tools And Techniques

    This section introduces various popular tools and techniques used in Explainable AI (XAI) to improve model interpretability.

    Learning Practice

  • 3.1

    Lime (Local Interpretable Model-Agnostic Explanations)

    LIME is a technique that helps to explain the predictions of complex AI models by approximating them with simpler models for individual predictions.

    Learning Practice

  • 3.2

    Shap (Shapley Additive Explanations)

    SHAP offers a framework derived from game theory for attributing model predictions to individual features, providing insights on how specific inputs influence outputs.

    Learning Practice

  • 3.3

    Partial Dependence Plots (Pdp)

    Partial Dependence Plots (PDP) visualize the relationship between a feature and the predicted outcome of a model, helping to interpret complex models.

    Learning Practice

  • 3.4

    Counterfactual Explanations

    Counterfactual explanations analyze how changes in input can alter model outcomes.

    Learning Practice

  • 4

    Interpretable Models Vs. Black Box Models

    This section examines the trade-offs between interpretable and black box models in AI, focusing on their respective performance levels and implications for transparency.

    Learning Practice

  • 5

    Xai In Practice

    This section highlights the practical applications of Explainable AI (XAI) across various sectors.

    Learning Practice

  • 6

    Ethics And Regulation

    This section emphasizes the significance of ethical standards and regulatory frameworks in the deployment of Explainable AI (XAI), focusing on transparency, fairness, and accountability.

    Learning Practice

References

Chapter 7_ Explainable AI (XAI) and Model Interpretability.pdf

Class Notes

Memorization

What we have learnt

  • XAI is essential to buildin...
  • Tools like SHAP and LIME he...
  • A balance is needed between...

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