Ethics-Aware Personalization: A Dual-Objective AI Framework for Engagement Optimization

AI-powered personalization systems have enhanced digital services by increasing user engagement, retention, and conversions. However, performance-maximizing personalization approaches compromise privacy, increase bias, and decrease transparency. This article uses a multipurpose personalization system that can simultaneously increase engagement and uphold ethics using Festinger's Social Comparison Theory and Skinner's Reinforcement Theory. The system consists of social comparison-based personalization modules, reinforcement learning, and an ethics-focused system layer for re-ranking, explainability, and privacy-preserving re-learning assumptions. The system's performance is validated using Python simulations for 1.2 million customer-level interaction data, showing improved proxy transparency and reduced equality in exposure bias with unchanged engagement performance. The research explains and creates the Ethical Experience Index (EEI), a measure of both engagement and ethics performance experiences for a systematic evaluation and comparison of performance. The results show the potential of integrating ethics systems for personalization that provide a repeatable, theory-based approach to ethics and AI-driven personalization based on simulations.