Contextual Bandits with Fairness Constraints for Personalized Ad Delivery at Scale

Personalized advertising has emerged as a dominant paradigm in digital marketing, leveraging online learning algorithms to optimize ad placement decisions in real-time. Contextual Multi-Armed Bandit (MAB) algorithms represent a principled approach to balancing the exploration-exploitation trade-off inherent in sequential decision-making, offering superior performance compared to traditional A/B testing methods in partial feedback environments. However, the deployment of such algorithms in user-facing applications raises critical concerns about fairness and potential discrimination across demographic groups. This paper addresses the challenge of incorporating fairness constraints into contextual bandit frameworks for large-scale personalized advertising systems. We position our work within the broader landscape of online learning paradigms, demonstrating how contextual bandits bridge the gap between simple multi-armed bandits and full reinforcement learning approaches. Through theoretical analysis and empirical validation, we propose Fair-Contextual UCB (FC-UCB), an algorithmic framework that extends the Linear Upper Confidence Bound principle with dynamic fairness penalty mechanisms. Our approach ensures group-level fairness while maintaining sublinear regret bounds comparable to unconstrained methods. Experimental results demonstrate that FC-UCB achieves fairness violations reduction of approximately 68% compared to standard LinUCB, while maintaining cumulative rewards within 4.2% of the optimal unconstrained performance. The robustness analysis reveals superior convergence properties compared to bootstrap-based approaches, with FC-UCB exhibiting consistent fairness maintenance across diverse problem instances. This work contributes both theoretical foundations and practical algorithms for deploying ethical and effective personalized advertising systems at scale.