Species ex machina: ‘the crush’ of animal data in AI
A canonical genealogy of artificial intelligence must include technologies and data being built with, for and from animals. Animal identification using forms of electronic monitoring and digital management began in the 1970s. Early data innovations comprised RFID tags and transponders that were foll...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2023-01-01
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| Series: | BJHS Themes |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2058850X23000073/type/journal_article |
| _version_ | 1827576336363814912 |
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| author | Simon Michael Taylor Syed Mustafa Ali Stephanie Dick Sarah Dillon Matthew L. Jones Jonnie Penn Richard Staley |
| author_facet | Simon Michael Taylor Syed Mustafa Ali Stephanie Dick Sarah Dillon Matthew L. Jones Jonnie Penn Richard Staley |
| author_sort | Simon Michael Taylor |
| collection | DOAJ |
| description | A canonical genealogy of artificial intelligence must include technologies and data being built with, for and from animals. Animal identification using forms of electronic monitoring and digital management began in the 1970s. Early data innovations comprised RFID tags and transponders that were followed by digital imaging and computer vision. Initially applied in the 1980s for agribusiness to identify meat products and to classify biosecurity data for animal health, yet computer vision is interlaced in subtler ways with commercial pattern recognition systems to monitor and track people in public spaces. As such this paper explores a set of managerial projects in Australian agriculture connected to computer vision and machine learning tools that contribute to dual-use. Herein, ‘the cattle crush’ is positioned as a pivotal space for animal bodies to be interrogated by AI imaging, digitization and data transformation with forms of computational and statistical analysis. By disentangling the kludge of numbering, imaging and classifying within precision agriculture the paper highlights a computational transference of techniques between species, institutional settings and domains that is relevant to regulatory considerations for AI development. The paper posits how a significant sector of data innovation – concerning uses on animals – may tend to evade some level of regulatory and ethical scrutiny afforded to human spaces and settings, and as such afford optimisation of these systems beyond our recognition. |
| first_indexed | 2024-03-08T21:09:48Z |
| format | Article |
| id | doaj.art-cba1e5f6b2a94c5facd8f122bacc570f |
| institution | Directory Open Access Journal |
| issn | 2058-850X 2056-354X |
| language | English |
| last_indexed | 2024-03-08T21:09:48Z |
| publishDate | 2023-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | BJHS Themes |
| spelling | doaj.art-cba1e5f6b2a94c5facd8f122bacc570f2023-12-22T09:11:49ZengCambridge University PressBJHS Themes2058-850X2056-354X2023-01-01815516910.1017/bjt.2023.7Species ex machina: ‘the crush’ of animal data in AISimon Michael Taylor0Syed Mustafa AliStephanie DickSarah DillonMatthew L. JonesJonnie PennRichard StaleySchool of Regulation and Global Governance, Australian National University, AustraliaA canonical genealogy of artificial intelligence must include technologies and data being built with, for and from animals. Animal identification using forms of electronic monitoring and digital management began in the 1970s. Early data innovations comprised RFID tags and transponders that were followed by digital imaging and computer vision. Initially applied in the 1980s for agribusiness to identify meat products and to classify biosecurity data for animal health, yet computer vision is interlaced in subtler ways with commercial pattern recognition systems to monitor and track people in public spaces. As such this paper explores a set of managerial projects in Australian agriculture connected to computer vision and machine learning tools that contribute to dual-use. Herein, ‘the cattle crush’ is positioned as a pivotal space for animal bodies to be interrogated by AI imaging, digitization and data transformation with forms of computational and statistical analysis. By disentangling the kludge of numbering, imaging and classifying within precision agriculture the paper highlights a computational transference of techniques between species, institutional settings and domains that is relevant to regulatory considerations for AI development. The paper posits how a significant sector of data innovation – concerning uses on animals – may tend to evade some level of regulatory and ethical scrutiny afforded to human spaces and settings, and as such afford optimisation of these systems beyond our recognition.https://www.cambridge.org/core/product/identifier/S2058850X23000073/type/journal_article |
| spellingShingle | Simon Michael Taylor Syed Mustafa Ali Stephanie Dick Sarah Dillon Matthew L. Jones Jonnie Penn Richard Staley Species ex machina: ‘the crush’ of animal data in AI BJHS Themes |
| title | Species ex machina: ‘the crush’ of animal data in AI |
| title_full | Species ex machina: ‘the crush’ of animal data in AI |
| title_fullStr | Species ex machina: ‘the crush’ of animal data in AI |
| title_full_unstemmed | Species ex machina: ‘the crush’ of animal data in AI |
| title_short | Species ex machina: ‘the crush’ of animal data in AI |
| title_sort | species ex machina the crush of animal data in ai |
| url | https://www.cambridge.org/core/product/identifier/S2058850X23000073/type/journal_article |
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