Learning Fair Demand Models
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 ...
| Authors | Adam N. Elmachtoub, Hyemi Kim, Jonathan Y. Tan |
|---|---|
| Primary category | cs.CY |
| Published | 5 June 2026 |
| Last updated at source | 5 June 2026 |
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