Authente-Kente: Enabling Authentication for Artisanal Economies with Deep Learning
dc.contributor.author | Robinson, Kwame | |
dc.contributor.author | Ron, Eglash | |
dc.contributor.author | Bennett, Audrey | |
dc.contributor.author | Nandakumar, Sansitha | |
dc.contributor.author | Robert, Lionel + "Jr" | |
dc.date.accessioned | 2020-08-20T13:02:17Z | |
dc.date.available | 2020-08-20T13:02:17Z | |
dc.date.issued | 2020-08-20 | |
dc.identifier.citation | Robinson, 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.2.2.27020.95362/2 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/156393 | |
dc.description.abstract | The 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.sponsorship | National Science Foundation (NSF) grant #1640014 | en_US |
dc.description.sponsorship | Mcubed grant #8330 | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | AI & Society | en_US |
dc.subject | human-machine collaboration | en_US |
dc.subject | machine learning | en_US |
dc.subject | artisanal economy | en_US |
dc.subject | generative justice | en_US |
dc.subject | industrial symbiosis | en_US |
dc.subject | ethnocomputing | en_US |
dc.subject | algorithm | en_US |
dc.subject | artificial intelligence | en_US |
dc.subject | artisanal products | en_US |
dc.subject | AI-based authentication | en_US |
dc.subject | West Africa | en_US |
dc.subject | Authente-Kente | en_US |
dc.subject | robotics | en_US |
dc.subject | mass-produced fakes | en_US |
dc.title | Authente-Kente: Enabling Authentication for Artisanal Economies with Deep Learning | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Information and Library Science | |
dc.subject.hlbtoplevel | Social Sciences | |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Information, School of | en_US |
dc.contributor.affiliationum | Penny W. Stamps School of Art and Design | en_US |
dc.contributor.affiliationum | Robotics Institute | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/156393/1/Robinson et al. 2020.pdf | en_US |
dc.identifier.doi | 10.13140/RG.2.2.27020.95362/2 | |
dc.identifier.source | AI & Society | en_US |
dc.identifier.orcid | 0000-0003-2663-571X | en_US |
dc.identifier.orcid | 0000-0003-1354-1300 | en_US |
dc.identifier.orcid | 0000-0002-6763-2622 | en_US |
dc.identifier.orcid | 0000-0001-8651-1415 | en_US |
dc.identifier.orcid | 0000-0002-1410-2601 | en_US |
dc.description.filedescription | Description of Robinson et al. 2020.pdf : PrePrint | |
dc.description.depositor | SELF | en_US |
dc.identifier.name-orcid | Robinson, Kwame; 0000-0003-2663-571X | en_US |
dc.identifier.name-orcid | Eglash, Ron; 0000-0003-1354-1300 | en_US |
dc.identifier.name-orcid | Bennett, Audrey; 0000-0002-6763-2622 | en_US |
dc.identifier.name-orcid | Nandakumar, Sansitha; 0000-0001-8651-1415 | en_US |
dc.identifier.name-orcid | Robert, Lionel P.; 0000-0002-1410-2601 | en_US |
dc.owningcollname | Information, School of (SI) |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.