Show simple item record

[journal article]

dc.contributor.authorGerdon, Fredericde
dc.contributor.authorBach, Ruben L.de
dc.contributor.authorKern, Christophde
dc.contributor.authorKreuter, Fraukede
dc.date.accessioned2025-04-28T11:08:29Z
dc.date.available2025-04-28T11:08:29Z
dc.date.issued2022de
dc.identifier.issn2053-9517de
dc.identifier.urihttps://www.ssoar.info/ssoar/handle/document/101902
dc.description.abstractAcademic and public debates are increasingly concerned with the question whether and how algorithmic decision-making (ADM) may reinforce social inequality. Most previous research on this topic originates from computer science. The social sciences, however, have huge potentials to contribute to research on social consequences of ADM. Based on a process model of ADM systems, we demonstrate how social sciences may advance the literature on the impacts of ADM on social inequality by uncovering and mitigating biases in training data, by understanding data processing and analysis, as well as by studying social contexts of algorithms in practice. Furthermore, we show that fairness notions need to be evaluated with respect to specific outcomes of ADM systems and with respect to concrete social contexts. Social sciences may evaluate how individuals handle algorithmic decisions in practice and how single decisions aggregate to macro social outcomes. In this overview, we highlight how social sciences can apply their knowledge on social stratification and on substantive domains of ADM applications to advance the understanding of social impacts of ADM.de
dc.languageende
dc.subject.ddcSozialwissenschaften, Soziologiede
dc.subject.ddcSocial sciences, sociology, anthropologyen
dc.subject.ddcTechnik, Technologiede
dc.subject.ddcTechnology (Applied sciences)en
dc.subject.otherfair machine learning; social impacts of AIde
dc.titleSocial impacts of algorithmic decision-making: A research agenda for the social sciencesde
dc.description.reviewbegutachtet (peer reviewed)de
dc.description.reviewpeer revieweden
dc.source.journalBig Data & Society
dc.source.volume9de
dc.publisher.countryUSAde
dc.source.issue1de
dc.subject.classozForschungsarten der Sozialforschungde
dc.subject.classozResearch Designen
dc.subject.classozTechnikfolgenabschätzungde
dc.subject.classozTechnology Assessmenten
dc.subject.thesozEntscheidungsfindungde
dc.subject.thesozdecision makingen
dc.subject.thesozAlgorithmusde
dc.subject.thesozalgorithmen
dc.subject.thesozsoziale Ungleichheitde
dc.subject.thesozsocial inequalityen
dc.subject.thesozkünstliche Intelligenzde
dc.subject.thesozartificial intelligenceen
dc.subject.thesozsoziale Folgende
dc.subject.thesozsocial effectsen
dc.subject.thesozUmfrageforschungde
dc.subject.thesozsurvey researchen
dc.subject.thesozDatenaufbereitungde
dc.subject.thesozdata preparationen
dc.subject.thesozAnalysede
dc.subject.thesozanalysisen
dc.rights.licenceCreative Commons - Namensnennung 4.0de
dc.rights.licenceCreative Commons - Attribution 4.0en
internal.statusformal und inhaltlich fertig erschlossende
internal.identifier.thesoz10042187
internal.identifier.thesoz10035039
internal.identifier.thesoz10038124
internal.identifier.thesoz10043031
internal.identifier.thesoz10043850
internal.identifier.thesoz10040714
internal.identifier.thesoz10040524
internal.identifier.thesoz10034712
dc.type.stockarticlede
dc.type.documentZeitschriftenartikelde
dc.type.documentjournal articleen
dc.source.pageinfo1-13de
internal.identifier.classoz10104
internal.identifier.classoz20800
internal.identifier.journal1669
internal.identifier.document32
internal.identifier.ddc300
internal.identifier.ddc600
dc.identifier.doihttps://doi.org/10.1177/20539517221089305de
dc.description.pubstatusVeröffentlichungsversionde
dc.description.pubstatusPublished Versionen
internal.identifier.licence16
internal.identifier.pubstatus1
internal.identifier.review1
internal.pdf.validfalse
internal.pdf.wellformedtrue
internal.pdf.encryptedfalse
ssoar.urn.registrationfalsede


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record