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dc.creatorBauer, Kevin
dc.creatorPfeuffer, Nicolas
dc.creatorAbdel-Karim, Benjamin M.
dc.creatorHinz, Oliver
dc.creatorKosfeld, Michael
dc.date.accessioned2021-09-28T09:40:55Z
dc.date.available2021-09-28T09:40:55Z
dc.date.issued2020-12-09
dc.identifier.urihttps://fif.hebis.de/xmlui/handle/123456789/2393
dc.description.abstractUsing a novel theoretical framework and data from a comprehensive field study we conducted over a period of three years, we outline the causal effects of algorithmic discrimination on economic efficiency and social welfare in a strategic setting under uncertainty. We combine economic, game-theoretic, and applied machine learning concepts allowing us to overcome the central challenge of missing counterfactuals, which generally impedes showcasing economic downstream consequences of algorithmic discrimination. Using our framework and unique data, we provide both theoretical and empirical evidence on the consequences of algorithmic discrimination. Our unique empirical setting allows us to precisely quantify efficiency and welfare ramifications relative to an ideal world where there are no information asymmetries. Our results emphasize that Artificial Intelligence systems' capabilities in overcoming information asymmetries and thereby enhancing welfare negatively depend on the degree of inherent algorithmic discrimination against specific groups in the population. This relation is particularly concerning in selective-labels environments where outcomes are only observed if decision-makers take a particular action so that the data is selectively labeled. The reason is that commonly used technical performance metrics like the precision measure can be highly deceptive and lead to wrong conclusions. Finally, our results depict that continued learning, by creating feedback loops, can help remedy algorithmic discrimination and associated negative effects over time.
dc.rightsAttribution-ShareAlike 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/
dc.subjectFinancial Intermediation
dc.subjectExperiment Center
dc.titleThe Economic Consequences of Algorithmic Discrimination: Theory and Empirical Evidence
dc.typeWorking Paper
dcterms.referenceshttps://fif.hebis.de/xmlui/handle/123456789/1836?Survey_ABHKP_2020
dc.source.filename287_SSRN-id3675313
dc.identifier.safeno287
dc.subject.keywordsalgorithmic discrimination
dc.subject.keywordssocial welfare
dc.subject.keywordseconomics
dc.subject.keywordsgame theory
dc.subject.keywordsfeedback loops
dc.subject.keywordsartificial intelligence
dc.subject.keywordsmachine learning
dc.subject.jelM20
dc.subject.topic1training
dc.subject.topic1fraud
dc.subject.topic1relatedly
dc.subject.topic2social
dc.subject.topic2enable
dc.subject.topic2interact
dc.subject.topic3instance
dc.subject.topic3trustee
dc.subject.topic3simplicity
dc.subject.topic1nameSystematic Risk
dc.subject.topic2nameMonetary Policy
dc.subject.topic3nameInvestor Behaviour
dc.identifier.doi10.2139/ssrn.3675313


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