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dc.contributor.author
von Zahn, Moritz
dc.contributor.author
Feuerriegel, Stefan
dc.contributor.author
Kuehl, Niklas
dc.date.accessioned
2021-09-28T11:32:39Z
dc.date.available
2021-06-09T13:14:10Z
dc.date.available
2021-06-09T14:11:50Z
dc.date.available
2021-09-28T11:32:39Z
dc.date.issued
2021
dc.identifier.issn
0937-6429
dc.identifier.issn
1867-0202
dc.identifier.issn
2363-7005
dc.identifier.other
10.1007/s12599-021-00716-w
dc.identifier.uri
http://hdl.handle.net/20.500.11850/488982
dc.description.abstract
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.
en_US
dc.language.iso
en
en_US
dc.publisher
Springer
en_US
dc.subject
AI fairness
en_US
dc.subject
Algorithmic fairness
en_US
dc.subject
Fair AI
en_US
dc.subject
Costs
en_US
dc.subject
Artificial intelligence
en_US
dc.subject
Machine learning
en_US
dc.title
The cost of fairness in AI: Evidence from e-commerce
en_US
dc.type
Journal Article
dc.date.published
2021-09-07
ethz.journal.title
Business & Information Systems Engineering
ethz.journal.abbreviated
Bus Inf Syst Eng
ethz.size
14 p.
en_US
ethz.grant
Governance and legal framework for managing artificial intelligence (AI)
en_US
ethz.identifier.wos
ethz.publication.place
Wiesbaden
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::09623 - Feuerriegel, Stefan (ehemalig) / Feuerriegel, Stefan (former)
en_US
ethz.grant.agreementno
197485
ethz.grant.agreementno
197485
ethz.grant.fundername
SNF
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
NFP 77: Gesuch
ethz.grant.program
NFP 77: Gesuch
ethz.date.deposited
2021-06-09T13:14:16Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.exportRequired
true
ethz.COinS
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