What technique provides insights into model predictions and aligns with Explainable AI practices?

Feature attribution is a technique that provides insights into model predictions by identifying and quantifying the contributions of each feature to the model's output. This is critical in Explainable AI practices, as it helps stakeholders understand the rationale behind a model's decision-making process. By utilizing feature attribution methods, one can decipher which features are most influential in a particular prediction, allowing for greater transparency and trust in machine learning models.

This approach can be particularly valuable in sensitive applications such as healthcare and finance, where understanding the decision process is essential for accountability and ethics. Techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are common examples of feature attribution methods used to achieve this transparency.

While techniques like feature engineering, hyperparameter tuning, and cross-validation are important for model performance and robustness, they do not focus on providing interpretability and insights into predictions in the same way that feature attribution does.