针对企业使用人工智能驱动定价中数据利用与消费者数据保护的矛盾，将消费者数据操纵行为纳入博弈分析框架，探究垄断企业最优定价策略、消费者数据操纵努力的最优决策，以及数据操纵行为产生的影响。研究表明：算法开发成本较低时，人工智能驱动定价利润最大；算法开发成本较高且高价值消费者较多时，统一高价最优；算法开发成本较高且高价值消费者较少时，统一低价最优。消费者数据操纵行为会压缩人工智能驱动定价的决策空间，降低算法投资意愿，增加消费者剩余；消费者数据操纵成本对AI定价策略空间具有扩张作用。

This study addresses the conflict between data utilization and consumer data protection in firms’ AI-driven pricing. A game-theoretic framework incorporating consumer data manipulation behavior is proposed. The monopolist’s optimal pricing strategy and consumers’ optimal data manipulation efforts, as well as the impact of data manipulation, are investigated. The results show that when the algorithm development cost is relatively low, the AI-driven pricing strategy yields the highest profit. When the algorithm development cost is relatively high and the proportion of high-value consumers is high, a uniform high price is optimal. When the algorithm development cost is relatively high and the proportion of high-value consumers is low, a uniform low price is optimal. Consumer data manipulation significantly reduces the decision space of AI-driven pricing, reduced the firm’s willingness to invest in algorithms, and increases consumer surplus, whereas the cost of consumer data manipulation has an expanding effect on the strategic space of AI pricing.