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Automation for the Artisanal Economy: Enhancing the Economic and Environmental Sustainability of Crafting Professions with Human-Machine Collaboration

dc.contributor.authorEglash, Ron
dc.contributor.authorRobert, Lionel + "Jr"
dc.contributor.authorBennett, Audrey
dc.contributor.authorRobinson, Kwame
dc.contributor.authorLachney, Michael
dc.contributor.authorBabbitt, William
dc.date.accessioned2019-08-08T21:22:59Z
dc.date.available2019-08-08T21:22:59Z
dc.date.issued2019-08-08
dc.identifier.citationEglash, R., Robert, L. P., Bennett, A., Robinson, K. P., Lachney, M. Babbitt, W., (accepted in 2019). Automation for the Artisanal Economy: Enhancing the Economic and Environmental Sustainability of Crafting Professions with Human-Machine Collaboration, AI & Society, accepteden_US
dc.identifier.urihttps://hdl.handle.net/2027.42/150492
dc.description.abstractArtificial intelligence (AI) is poised to eliminate millions of jobs, from finance to truck driving. But artisanal products—(e.g. handmade textiles) are valued precisely because of their human origins, and thus have some inherent “immunity” from AI job loss. At the same time, artisanal labor, combined with technology, could potentially help to democratize the economy, allowing independent, small scale businesses to flourish. Could AI, robotics and related automation technologies enhance the economic viability and environmental sustainability of these beloved crafting professions, perhaps even expanding their niche to replace some job loss in other sectors? In this paper we compare the problems created by the current mass production economy, and potential solutions from an artisanal economy. In doing so, the paper details the possibilities of utilizing AI to support hybrid forms of human-machine production at the micro-scale; localized and sustainable value chains at the meso-scale; and networks of these localized and sustainable producers at the macro scale. In short, a wide range of automation technologies are potentially available for facilitating and empowering an artisanal economy. Ultimately, it is our hope that this paper will facilitate a discussion on a future vision for more “generative” economic forms in which labor value, ecological value and social value can circulate without extraction or alienation.en_US
dc.description.sponsorshipNational Science Foundation DRL-1640014en_US
dc.description.sponsorshipNational Science Foundation DGE-0947980en_US
dc.language.isoen_USen_US
dc.publisherAI & Societyen_US
dc.subjecthuman-machine collaborationen_US
dc.subjectartisanal economyen_US
dc.subjectgenerative justiceen_US
dc.subjectindustrial symbiosisen_US
dc.subjectethnocomputingen_US
dc.subjectArtificial intelligenceen_US
dc.subjecthuman-machine productionen_US
dc.subjectFuture of Worken_US
dc.subjectArtificial intelligence Laboren_US
dc.subjectlabor alienationen_US
dc.subjectecological alienationen_US
dc.subjectsocial alienationen_US
dc.subjectjob alienationen_US
dc.subjectheritage algorithmsen_US
dc.subjectartisan-machine hybridityen_US
dc.subjectupskillingen_US
dc.subjectartisanal economy value chainsen_US
dc.subjectautomation and artisansen_US
dc.subjectartisanal productionen_US
dc.titleAutomation for the Artisanal Economy: Enhancing the Economic and Environmental Sustainability of Crafting Professions with Human-Machine Collaborationen_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.affiliationumSchool of Art and Designen_US
dc.contributor.affiliationotherMichigan State Universityen_US
dc.contributor.affiliationotherRensselaer Universityen_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/150492/1/Eglash et al. accepted in 2019.pdf
dc.identifier.sourceAI & Societyen_US
dc.identifier.orcid0000-0003-1354-1300en_US
dc.identifier.orcid0000-0002-1410-2601en_US
dc.identifier.orcid0000-0002-6763-2622en_US
dc.identifier.orcid0000-0003-2663-571Xen_US
dc.identifier.orcid0000-0003-3310-8707en_US
dc.identifier.orcid0000-0002-2684-4901en_US
dc.description.filedescriptionDescription of Eglash et al. accepted in 2019.pdf : Preprint Version
dc.identifier.name-orcidLachney, Michael; 0000-0003-3310-8707en_US
dc.identifier.name-orcidBabbitt, William; 0000-0002-2684-4901en_US
dc.identifier.name-orcidEglash, Ron; 0000-0003-1354-1300en_US
dc.identifier.name-orcidRobert, Lionel P.; 0000-0002-1410-2601en_US
dc.identifier.name-orcidBennett, Audrey; 0000-0002-6763-2622en_US
dc.identifier.name-orcidRobinson, Kwame; 0000-0003-2663-571Xen_US
dc.owningcollnameInformation, School of (SI)


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