Wrong Model or Wrong Practices? Mis-specified Demand Model and Algorithmic Bias in Personalized Pricing

Abstract

The societal significance of fair machine learning (ML) cannot be overstated, yet quantifying algorithmic bias and ensuring fair ML remains a challenging task. One popular fair ML objective, equality of opportunity, requires equal treatment for individuals who are equally deserving, regardless of their group affiliation. However, determining who should be considered "equally deserving" is a complex and critical aspect that directly affects the estimation of algorithmic bias. This paper emphasizes the importance of accurately measuring equal deservingness in order to accurately estimate algorithmic bias. To illustrate this, the paper examines the case of personalized pricing and shows that assuming a mis-specified model for equal deservingness can result in incorrect bias estimates. Using a detailed consumer data set from a large e-commerce platform, the paper demonstrates that when the correct consumer demand model is a non-sequential search model where consumers differ in their search costs based on gender, assuming a standard choice demand model or a traditional ML (e.g. Support Vector Machine) can lead to incorrect bias estimates. This research highlights the critical role that a proper model specification plays in achieving fair ML practices.

Keywords: Algorithmic Bias, Fairness, Personalized Pricing, Structural modeling