When Machine Learning Gets Personal: Understanding Fairness of Personalized Models
This work addresses fairness and interpretability issues in personalized models for critical domains like healthcare, though it is incremental as it builds on existing evaluation frameworks.
The paper tackles the problem of evaluating fairness and explainability in personalized machine learning models, showing that improvements in prediction accuracy from personalization do not necessarily enhance explainability, with regression models potentially using more personal attributes than classification models.
Personalization in machine learning involves tailoring models to individual users by incorporating personal attributes such as demographic or medical data. While personalization can improve prediction accuracy, it may also amplify biases and reduce explainability. This work introduces a unified framework to evaluate the impact of personalization on both prediction accuracy and explanation quality across classification and regression tasks. We derive novel upper bounds for the number of personal attributes that can be used to reliably validate benefits of personalization. Our analysis uncovers key trade-offs. We show that regression models can potentially utilize more personal attributes than classification models. We also demonstrate that improvements in prediction accuracy due to personalization do not necessarily translate to enhanced explainability -- underpinning the importance to evaluate both metrics when personalizing machine learning models in critical settings such as healthcare. Validated with a real-world dataset, this framework offers practical guidance for balancing accuracy, fairness, and interpretability in personalized models.