Model explainability refers to the ability to understand and interpret the predictions made by a machine learning model.

It aims to provide insights into how a model arrives at its conclusions

Explainability is important for building trust in AI systems, especially in critical domains like healthcare and finance.

It helps in identifying biases, errors, and limitations in the model's predictions.

Interpretable models can aid in compliance with legal and ethical regulations

Techniques for model explainability can vary depending on the model type and complexity.

Feature importance analysis is a common method used for explainability.

Rule-based explanations, such as decision trees or rule lists, can provide a human-readable representation of the model's decision-making process.