Automation for the Artisanal Economy: Enhancing the Economic and Environmental Sustainability of Crafting Professions with Human-Machine Collaboration
dc.contributor.author | Eglash, Ron | |
dc.contributor.author | Robert, Lionel + "Jr" | |
dc.contributor.author | Bennett, Audrey | |
dc.contributor.author | Robinson, Kwame | |
dc.contributor.author | Lachney, Michael | |
dc.contributor.author | Babbitt, William | |
dc.date.accessioned | 2019-08-08T21:22:59Z | |
dc.date.available | 2019-08-08T21:22:59Z | |
dc.date.issued | 2019-08-08 | |
dc.identifier.citation | Eglash, 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, accepted | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/150492 | |
dc.description.abstract | Artificial 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.sponsorship | National Science Foundation DRL-1640014 | en_US |
dc.description.sponsorship | National Science Foundation DGE-0947980 | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | AI & Society | en_US |
dc.subject | human-machine collaboration | 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 | Artificial intelligence | en_US |
dc.subject | human-machine production | en_US |
dc.subject | Future of Work | en_US |
dc.subject | Artificial intelligence Labor | en_US |
dc.subject | labor alienation | en_US |
dc.subject | ecological alienation | en_US |
dc.subject | social alienation | en_US |
dc.subject | job alienation | en_US |
dc.subject | heritage algorithms | en_US |
dc.subject | artisan-machine hybridity | en_US |
dc.subject | upskilling | en_US |
dc.subject | artisanal economy value chains | en_US |
dc.subject | automation and artisans | en_US |
dc.subject | artisanal production | en_US |
dc.title | Automation for the Artisanal Economy: Enhancing the Economic and Environmental Sustainability of Crafting Professions with Human-Machine Collaboration | 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 | School of Art and Design | en_US |
dc.contributor.affiliationother | Michigan State University | en_US |
dc.contributor.affiliationother | Rensselaer University | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/150492/1/Eglash et al. accepted in 2019.pdf | |
dc.identifier.source | AI & Society | en_US |
dc.identifier.orcid | 0000-0003-1354-1300 | en_US |
dc.identifier.orcid | 0000-0002-1410-2601 | en_US |
dc.identifier.orcid | 0000-0002-6763-2622 | en_US |
dc.identifier.orcid | 0000-0003-2663-571X | en_US |
dc.identifier.orcid | 0000-0003-3310-8707 | en_US |
dc.identifier.orcid | 0000-0002-2684-4901 | en_US |
dc.description.filedescription | Description of Eglash et al. accepted in 2019.pdf : Preprint Version | |
dc.identifier.name-orcid | Lachney, Michael; 0000-0003-3310-8707 | en_US |
dc.identifier.name-orcid | Babbitt, William; 0000-0002-2684-4901 | en_US |
dc.identifier.name-orcid | Eglash, Ron; 0000-0003-1354-1300 | en_US |
dc.identifier.name-orcid | Robert, Lionel P.; 0000-0002-1410-2601 | en_US |
dc.identifier.name-orcid | Bennett, Audrey; 0000-0002-6763-2622 | en_US |
dc.identifier.name-orcid | Robinson, Kwame; 0000-0003-2663-571X | en_US |
dc.owningcollname | Information, School of (SI) |
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