Data-driven pricing is increasingly prevalent in sectors such as airlines, lending, insurance, and retail. By learning demand models from customer features and setting prices accordingly, these systems may generate discriminatory outcomes that raise fairness concerns. This leads to fundamental questions - how and where should systems incorporate fairness considerations in the pricing pipeline, and how does it ultimately affect societal outcomes? To answer these, we study a stylized model where a seller has a two-stage decision pipeline comprising linear demand model estimation followed by price optimization. The seller considers fairness notions in training loss, price, and demand, under both parity-wise and Rawlsian perspectives. We show that equalizing training loss across consumer groups leads to multiple solutions, which in turn can result in undesirable outcomes despite being a standard approach in fair machine learning. Focusing instead on fairness applied directly to prices or demand, we compare two strategies that enforce fairness in either the demand estimation stage or the price optimization stage. For parity-wise fairness, we characterize when each strategy yields higher social welfare under small fairness levels. We show that when market sizes and prices in the dataset are similar, imposing price fairness in the estimation stage is more beneficial to consumers, whereas imposing demand fairness in the optimization stage yields better consumer outcomes. For Rawlsian fairness, the two strategies coincide exactly. Lastly, we extend our model to alternate demand functions and conduct a case study using real-world vaccine pricing data.

翻译：数据驱动定价在航空、借贷、保险和零售等领域日益普及。通过从客户特征中学习需求模型并据此设定价格，这些系统可能产生引发公平性担忧的歧视性结果。这引发了一系列基本问题——系统应在定价流程的何处以及如何纳入公平性考量，以及这最终将如何影响社会结果？为回答这些问题，我们研究了一个简洁模型，其中卖方采用包含线性需求模型估计和价格优化两个阶段的决策流程。卖方在训练损失、价格和需求方面，从均等主义和罗尔斯主义两种视角考虑公平性概念。我们证明，跨消费者群体均等化训练损失会导致多重解，而尽管这是公平机器学习中的标准方法，却可能产生不良结果。我们转而关注直接应用于价格或需求的公平性，比较了在需求估计阶段或价格优化阶段实施公平性的两种策略。对于均等主义公平，我们刻画了在低公平性水平下每种策略何时能带来更高的社会福利。研究表明，当数据集中市场规模和价格相似时，在估计阶段施加价格公平性对消费者更有利，而在优化阶段施加需求公平性则能带来更好的消费者结果。对于罗尔斯主义公平，两种策略完全一致。最后，我们将模型扩展至替代需求函数，并利用真实疫苗定价数据进行了案例研究。