Fairness-aware Contextual Dynamic Pricing with Strategic Buyers

Researchers Pangpang Liu and Will Wei Sun have published a paper titled "Fairness-aware Contextual Dynamic Pricing with Strategic Buyers," presenting a novel AI model designed to tackle a critical dual challenge in automated pricing. The system addresses the prevalent issue of algorithmic price discrimination—where prices vary unfairly based on sensitive attributes like race or gender—while simultaneously preventing strategic buyers from gaming the system by misrepresenting their group identity to secure lower prices. This is a significant advancement over standard contextual pricing, which optimizes for profit but often entrenches bias and can be exploited.

The proposed dynamic pricing policy mathematically enforces fairness constraints and is proven to achieve a regret upper bound of O(√T), matching the theoretical lower bound for this problem class, meaning it's provably efficient. In practical tests using real loan application data, which showed evidence of racial price discrimination even after accounting for other factors, the model outperformed benchmarks by reducing regret—a measure of lost profit or fairness violation—by 35.06%. This demonstrates its effectiveness in real-world scenarios where fairness, regulatory compliance, and profit must be balanced.