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, by which AI is adapted such that mathematical constraints for fairness are fulfilled. However, the financial costs of AI fairness are unclear. Therefore, the authors develop AI fairness for a real-world use case from e-commerce, where coupons are allocated according to clickstream sessions. In their setting, the authors find that AI fairness successfully manages to adhere to fairness requirements, while reducing the overall prediction performance only slightly. However, they find that AI fairness also results in an increase in financial cost. Thus, in this way the paper’s findings contribute to designing information systems on the basis of AI fairness.

当代信息系统广泛使用人工智能 (AI)。虽然人工智能提供了各种好处，但它也可能受到系统错误的影响，从而使来自某些群体（由性别、年龄或其他敏感属性定义）的人体验到不同的结果。在许多人工智能应用中，企业和组织面临法律和声誉风险的不同结果。为了解决这些问题，已经开发了所谓的“AI 公平性”技术，通过该技术对 AI 进行调整，从而满足公平性的数学约束。然而，人工智能公平的财务成本尚不清楚。因此，作者为电子商务的真实用例开发了 AI 公平性，其中根据点击流会话分配优惠券。在他们的设定中，作者发现 AI 公平性成功地满足了公平性要求，同时仅略微降低了整体预测性能。然而，他们发现 AI 公平性也会导致财务成本的增加。因此，通过这种方式，本文的研究结果有助于在 AI 公平性的基础上设计信息系统。