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AI & SOCIETY

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02.09.2020 | OPEN FORUM

Authente-Kente: enabling authentication for artisanal economies with deep learning

verfasst von: Kwame Porter Robinson, Ron Eglash, Audrey Bennett, Sansitha Nandakumar, Lionel Robert

Erschienen in: AI & SOCIETY | Ausgabe 1/2021

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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.

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A case can be made that the print is not intended to be mistaken for handwoven, and therefore legitimately sold. But we have observed tourists failing to grasp the distinction, so the impact on weavers is the same. There are also distinctions between “authentic prints” made in Ghana, and those produced in foreign countries and smuggled across the border, but that is beyond the scope of this paper.

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See https://github.com/robinsonkwame/kente-cloth-authentication.

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Titel: Authente-Kente: enabling authentication for artisanal economies with deep learning
verfasst von: Kwame Porter Robinson
Ron Eglash
Audrey Bennett
Sansitha Nandakumar
Lionel Robert
Publikationsdatum: 02.09.2020
Verlag: Springer London
Erschienen in: AI & SOCIETY / Ausgabe 1/2021
Print ISSN: 0951-5666
Elektronische ISSN: 1435-5655
DOI: https://doi.org/10.1007/s00146-020-01055-2

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