The cost of fairness in AI: Evidence from e-commerce

Contemporary information systems make widespread use of artificial intelligence (AI). While AI offers various benefits, it can also be subject to systematic errors, whereby people from certain groups (defined by gender, age, or other sensitive attributes) experience disparate outcomes. In many AI applications, disparate outcomes confront businesses and organizations with legal and reputational risks. To address these, technologies for so-called “AI fairness” have been developed, whereby AI is adapted such that mathematical constraints for fairness are fulfilled. However, the financial costs of AI fairness are unclear. Therefore, we develop AI fairness for a real-world use case from e-commerce, where coupons are allocated according to clickstream sessions. In our setting, we find that AI fairness successfully manages to adhere to fairness requirements, while reducing the overall prediction performance only slightly. However, we find that AI fairness also results in an increase in financial cost. Thereby, our findings contribute to designing information systems on the basis of AI fairness.