Machine Learning Content Personalization Risk Assessment Framework

Develop a comprehensive machine learning risk assessment framework for content recommendation algorithms that prevents potential filter bubbles and maintains content diversity. Create an explainable AI model that tracks recommendation drift, implements ethical recommendation boundaries, and provides transparent scoring for content suggestions. Include mechanisms to detect and mitigate potential algorithmic bias across different user demographics.