TY  - JOUR
A1  - Zahn, Moritz von
A1  - Feuerriegel, Stefan
A1  - Kühl, Niklas
T1  - The cost of fairness in AI: evidence from e-commerce
T2  - Business & information systems engineering
N2  - 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.
KW  - AI fairness
KW  - Algorithmic fairness
KW  - Fair AI
KW  - Costs
KW  - Artificial intelligence
KW  - Machine learning
Y1  - 2021
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/63561
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-635613
SN  - 2363-7005
N1  - Open Access funding enabled and organized by Projekt DEAL.
N1  - Stefan Feuerriegel acknowledges support from the Swiss National Science Foundation (Grant 197485).
N1  - Early View: Online Version before inclusion in an issue
VL  - 2021
PB  - AIS ;  Springer Gabler
CY  - Atlanta, Georgia ; Wiesbaden
ER  -