Authente-Kente: enabling authentication for artisanal economies with deep learning
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Authente‑Kente: enabling authentication for artisanal economies with deep learning Kwame Porter Robinson1 · Ron Eglash1 · Audrey Bennett2 · Sansitha Nandakumar1 · Lionel Robert1 Received: 24 June 2020 / Accepted: 18 August 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
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. Keywords Human–machine collaboration · Machine learning · Artisanal economy · Generative justice · Industrial symbiosis · Ethnocomputing
1 Introduction Mass production economies have introduced many ills into social life, including high rates of mental, and physical illness from dull repetitive jobs; high rates of environmental degradation; and a “junk food” approach to both physical and informational over-consumption (Michelsen and Bildt 2003; Coccia 2017; Hunt et al. 2018). Artisanal fabrication, in contrast, tends to embody the opposite effect. Artisans often report that they are drawn to their craft because it is an enjoyable and rewarding form of labor. Traditional fabrication methods often use locally sourced and sustainable supply chains. And (at least traditionally) artisanal items * Kwame Porter Robinson [email protected] Ron Eglash [email protected] Audrey Bennett [email protected] Sansitha Nandakumar [email protected] Lionel Robert [email protected] 1
University of Michigan School of Information, 3360 North Quad, 105 S. State St., Ann Arbor, MI 48109‑1285, USA
University of Michigan Penny W. Stamps School of Art and Design, Ann Arbor, USA
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were purchased in more thoughtful ways, often establishing a personal relationship between buyer and seller. In our prior work (Eglash et al. 2019), we suggested that AI, robotics, and other forms of automation, if properly designed and implemented, could gradually scale these beneficial systems towards the development of an artisanal economy. One small step in that direction might be AI guides that help connect consumers with artisanal producers. In this paper, we explore a prototype, Authente-Kente, to help guide consumers toward selection of authentic hand-woven kente cloth, and thus diminish income loss due to mass produced fake cloth.
2 The problem context Traditional artisanal items often compete with mass-produced fakes. M’Closkey (2010) estimates that out of roughly 2 billion in annual sales of “Native American” goods, about 50% is not actually of Native origin. Similar problems arise elsewhere: for example, Mehra (2019) reports th
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