AI for a Generative Economy: The Role of Intelligent Systems in Sustaining Unalienated Labor, Environment, and Society
dc.contributor.author | Eglash, Ron | |
dc.contributor.author | Robert, Lionel | |
dc.contributor.author | Bennett, Audrey | |
dc.contributor.author | Robinson, Kwame | |
dc.contributor.author | Lachney, Michael | |
dc.contributor.author | Babbitt, William | |
dc.date.accessioned | 2019-08-25T14:34:23Z | |
dc.date.available | 2019-08-25T14:34:23Z | |
dc.date.issued | 2019-08-25 | |
dc.identifier.citation | Eglash, R., Robert, L. P., Bennett, A., Robinson, K. P., Lachney, M. and Babbitt, W. (2019). AI for a Generative Economy: Intelligent Systems and Work in a Just and Sustainable Future presented at AAAI Fall Symposium on AI and Work, Arlington, Virginia USA, November 7-9, 2019. | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/150647 | |
dc.description.abstract | Extractive economies pull value from a system without restoring it. Unsustainable extraction of ecological value includes over-fishing, clear-cut logging, etc. Extraction of labor value is similarly objectionable: assembly line jobs for example increase the likelihood of cardiovascular disease, depression, suicide and other problems. Extraction of social value--vacuuming up online personal information, commodification of the public sphere, and so on-- constitutes a third form. But all three domains--ecological value, labor value, and social value--can thrive in unalienated forms if we can create a future of work that replaces extraction with generative cycles. AI is a key technology in developing these alternative economic forms. This paper describes some initial experiments with African, African American, and Native American artisans who were willing to experiment with the introduction of computational enhancements to their work. Following our report on these initial results, we map out a vision for how AI could scale up labor that sustains “heritage algorithms”, ecologically situated value chains and other hybrid forms that prevent value alienation while flourishing from its robust circulation. | en_US |
dc.description.sponsorship | NSF grant DRL-1640014 | en_US |
dc.description.sponsorship | NSF grant DGE-0947980 | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | AAAI Fall Symposium Series | en_US |
dc.relation.ispartofseries | AAAI Fall Symposium Series (2019) | 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 | AI | en_US |
dc.subject | Artificial Intelligence | en_US |
dc.subject | ecological value | en_US |
dc.subject | labor value | en_US |
dc.subject | social value | en_US |
dc.subject | alternative economic forms | en_US |
dc.subject | Collaborative robots | en_US |
dc.subject | cobots | en_US |
dc.subject | monotony | en_US |
dc.subject | Alienation of ecological value | en_US |
dc.subject | Alienation of social value | en_US |
dc.subject | artisanal cyborgs | en_US |
dc.subject | social computing | en_US |
dc.subject | artificial intelligence and equality | en_US |
dc.subject | artificial intelligence and fairness | en_US |
dc.subject | human computer interaction | en_US |
dc.subject | computer supported collaborative work | en_US |
dc.title | AI for a Generative Economy: The Role of Intelligent Systems in Sustaining Unalienated Labor, Environment, and Society | en_US |
dc.type | Conference Paper | 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 Polytechnic Institute | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/150647/1/FSS-19_paper_64.pdf | |
dc.identifier.source | AAAI Fall 2019 Symposium on AI and Work | 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 FSS-19_paper_64.pdf : Preprint Version | |
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 | Robinson, Kwame; 0000-0003-2663-571X | en_US |
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 | Robert, Lionel P.; 0000-0002-1410-2601 | en_US |
dc.owningcollname | Information, School of (SI) |
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