Understanding Benefit of Personalization: Beyond Classification

This work introduces a unified framework to evaluate personalization in machine learning by jointly quantifying its impact on predictive performance and explanation quality across groups, demonstrating theoretically and empirically that gains in accuracy do not necessarily imply improved explainability, even in safety-critical settings.