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Authente-Kente: Enabling Authentication for Artisanal Economies with Deep Learning

dc.contributor.authorRobinson, Kwame
dc.contributor.authorRon, Eglash
dc.contributor.authorBennett, Audrey
dc.contributor.authorNandakumar, Sansitha
dc.contributor.authorRobert, Lionel + "Jr"
dc.date.accessioned2020-08-20T13:02:17Z
dc.date.available2020-08-20T13:02:17Z
dc.date.issued2020-08-20
dc.identifier.citationRobinson, K.P., Eglash, R., Bennett, A., Nandakumar, S. and Robert, L.P. (2020). Authente-Kente: Enabling Authentication for Artisanal Economies with Deep Learning, AI & Society, 10.13140/RG.2.2.27020.95362/2en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/156393
dc.description.abstractThe economy for artisanal products such as Navajo rugs or Pashmina shawls are often threatened by mass-produced fakes. We propose the use of AI-based authentication as one part of a larger system that would replace extractive economies with generative circulation. In this case study we examine initial experiments towards the development of a cell phone based authentication app for kente cloth in west Africa. We describe the context of weavers and cloth sales; an initial test of a machine learning algorithm for distinguishing between real and fake kente, and an outline of the next stages of development.en_US
dc.description.sponsorshipNational Science Foundation (NSF) grant #1640014en_US
dc.description.sponsorshipMcubed grant #8330en_US
dc.language.isoen_USen_US
dc.publisherAI & Societyen_US
dc.subjecthuman-machine collaborationen_US
dc.subjectmachine learningen_US
dc.subjectartisanal economyen_US
dc.subjectgenerative justiceen_US
dc.subjectindustrial symbiosisen_US
dc.subjectethnocomputingen_US
dc.subjectalgorithmen_US
dc.subjectartificial intelligenceen_US
dc.subjectartisanal productsen_US
dc.subjectAI-based authenticationen_US
dc.subjectWest Africaen_US
dc.subjectAuthente-Kenteen_US
dc.subjectroboticsen_US
dc.subjectmass-produced fakesen_US
dc.titleAuthente-Kente: Enabling Authentication for Artisanal Economies with Deep Learningen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelInformation and Library Science
dc.subject.hlbtoplevelSocial Sciences
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumInformation, School ofen_US
dc.contributor.affiliationumPenny W. Stamps School of Art and Designen_US
dc.contributor.affiliationumRobotics Instituteen_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/156393/1/Robinson et al. 2020.pdfen_US
dc.identifier.doi10.13140/RG.2.2.27020.95362/2
dc.identifier.sourceAI & Societyen_US
dc.identifier.orcid0000-0003-2663-571Xen_US
dc.identifier.orcid0000-0003-1354-1300en_US
dc.identifier.orcid0000-0002-6763-2622en_US
dc.identifier.orcid0000-0001-8651-1415en_US
dc.identifier.orcid0000-0002-1410-2601en_US
dc.description.filedescriptionDescription of Robinson et al. 2020.pdf : PrePrint
dc.description.depositorSELFen_US
dc.identifier.name-orcidRobinson, Kwame; 0000-0003-2663-571Xen_US
dc.identifier.name-orcidEglash, Ron; 0000-0003-1354-1300en_US
dc.identifier.name-orcidBennett, Audrey; 0000-0002-6763-2622en_US
dc.identifier.name-orcidNandakumar, Sansitha; 0000-0001-8651-1415en_US
dc.identifier.name-orcidRobert, Lionel P.; 0000-0002-1410-2601en_US
dc.owningcollnameInformation, School of (SI)


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