Explainable AI (XAI) Fundamentals

The Core Idea

Explainable AI (XAI) focuses on developing methods that allow humans to understand and trust the decisions made by artificial intelligence models.

Trust and Acceptance: The Role of XAI

XAI enhances trust and acceptance of AI models by providing explanations for their decision-making processes at both global and local levels.

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Global vs Local: Feature Importance, Partial Dependencies, SHAP/LIME, and Counterfactuals

Understanding the distinction between global and local feature importance is crucial. Techniques like SHAP and LIME analyze partial dependencies, while counterfactual explanations offer insights into what changes would alter a prediction.

Frequently asked questions

What factors should be considered when choosing an XAI method for a given model (e.g., decision trees versus neural networks)?

When selecting an XAI method, consider the stability of the explanations produced, alignment with domain knowledge, and the ability to communicate effectively with users. The choice depends heavily on the complexity and type of model being used.

Why are local explanation methods sometimes unstable and prone to manipulation?

Local explanation methods can be unstable due to their sensitivity to small changes in the input data. Furthermore, these methods can be manipulated to create misleading explanations – a phenomenon often referred to as ‘fake trust’ without genuine improvements in model quality.

What does XAI encompass – are these simply tools or a broader process?

XAI represents both the tools and processes needed for responsible AI development. Crucially, it involves integrating explanations into the product interaction to ensure transparency and accountability.

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Everything above runs in your browser — open Gradient Descent Visualiser and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

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