When Machine Learning Gets Personal: Evaluating Models' Prediction and Explanation

Cornelis, L., Bernárdez, G., Jeong, H., Miolane, N.

This work introduces a unified framework to assess how personalization using individual attributes impacts both accuracy and explainability in machine learning, revealing key trade-offs and offering guidance for balanced, fair, and interpretable models in critical areas like healthcare.