Explainable AI (XAI): Model-Agnostic Approaches to Explainability - Pros and Cons

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Model-agnostic approaches to explainability refer to techniques that can be applied to interpret the predictions of any machine learning model, regardless of its underlying architecture or complexity. While these approaches offer flexibility and can be applied to a wide range of models, they also come with their own set of advantages and limitations. Let’s explore the pros and cons of model-agnostic explainability methods:

I. Pros:

1. Versatility:

• Pro: Model-agnostic approaches are versatile and can be applied to interpret any type of machine learning model, including black-box models like deep neural networks or complex ensemble models.

2. Interpretability for Diverse Models:

• Pro: Model-agnostic methods provide a consistent framework for interpreting predictions across different model types, making them suitable for heterogeneous machine learning ecosystems.

3. Post hoc Interpretation:

• Pro: Model-agnostic approaches operate post hoc, meaning they can be applied to already trained models without requiring modifications to the training process. This allows for the interpretation of pre-existing…