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Model Explainability Tools

[ˈmɒdl ɪkˌspleɪnəˈbɪlɪti tuːlz]
AI Infrastructure
Last updated: December 9, 2024

Definition

Software and techniques that help understand how AI models make decisions.

Detailed Explanation

A collection of methods and frameworks designed to provide insights into the decision-making processes of machine learning models. These tools help developers and users understand model behavior, validate predictions, and identify potential biases or issues in model performance.

Use Cases

Debugging AI systems, regulatory compliance in financial services, validating medical diagnosis models, ensuring fairness in hiring algorithms

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