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dc.contributor.authorLambrecht, Anja
dc.contributor.authorTucker, Catherine
dc.date.accessioned2022-08-05T16:39:04Z
dc.date.available2022-08-05T16:39:04Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/1721.1/144250
dc.description.abstract<jats:title>Abstract</jats:title> <jats:p>Some algorithms may have similar discriminatory tendencies to humans. The presented study investigates gender bias in social media advertising in the context of STEM careers. Results suggest that advertising algorithms are not gender-biased as such, but that economic forces in the background might lead to unintended, uneven outcomes. Spillover effects across industries make reaching some consumer segments more likely than others. A gender-neutral strategy is less likely to reach women because women are more likely to react to advertising. Therefore, targeting them is more expensive and economic forces unintentionally favor men. One potential solution could be running separate campaigns for men and women to target both demographic groups equally. However, anti-discrimination legislation in many countries does not allow companies to target employment ads to only one gender. So ironically, laws that are designed to avoid discrimination actually rule out a fairly simple way to correct the bias in online targeting on Facebook and other platforms, illustrating further need for policy guidance in this area.</jats:p>en_US
dc.language.isoen
dc.publisherWalter de Gruyter GmbHen_US
dc.relation.isversionof10.2478/NIMMIR-2021-0004en_US
dc.rightsCreative Commons Attribution NonCommercial License 4.0en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/en_US
dc.sourceSciendoen_US
dc.titleAlgorithm-Based Advertising: Unintended Effects and the Tricky Business of Mitigating Adverse Outcomesen_US
dc.typeArticleen_US
dc.identifier.citationLambrecht, Anja and Tucker, Catherine. 2021. "Algorithm-Based Advertising: Unintended Effects and the Tricky Business of Mitigating Adverse Outcomes." NIM Marketing Intelligence Review, 13 (1).
dc.contributor.departmentSloan School of Management
dc.relation.journalNIM Marketing Intelligence Reviewen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-08-05T16:35:27Z
dspace.orderedauthorsLambrecht, A; Tucker, Cen_US
dspace.date.submission2022-08-05T16:35:28Z
mit.journal.volume13en_US
mit.journal.issue1en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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