She has a thousand words well-weighted
more than present day pictures
generated in the millions
She is countless
more than the mathematical functions
running markets and subprime geopolitics
She gives immeasurably
more than the transnational reported data
ignoring the outlier old lady next door
She serves quietly, not subdued
louder than megaphones and limelight
yet fiercely, fist firm and thread strong
she is unnamed unseen yet in the face
the curtains hiding wizards, she is
the rows of workers outsourced, she seats
She echoes through what you don’t see
The glitches you don’t feel
The hell frozen over behind your screen
She calms the digital tsunami
the dark web kraken
she’s the tired keeper of our online seas
—animasuri’24—-•
triggers
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Bhutani Vij, A. (2023). Women Workers Behind the AI Revolution: The Production and Reproduction of Data Annotation Platforms. ISBN9798380833219. Dissertation/thesis number 30567667. PhD Dissertation: University of Toronto, Ontario, CA, Canada. https://www.proquest.com/dissertations-theses/women-workers-behind-ai-revolution-production/docview/2889570189/se-2?accountid=14511
Goetze, T. S. (2024). AI Art is Theft: Labour, Extraction, and Exploitation, Or, On the Dangers of Stochastic Pollocks. arXiv.Org. https://doi.org/10.48550/arxiv.2401.06178
Morreale, F., Bahmanteymouri, E., Burmester, B., Chen, A., & Thorp, M. (2023). The unwitting labourer: extracting humanness in AI training. AI & Society. https://doi.org/10.1007/s00146-023-01692-3
Nedzhvetskaya, Nataliya, and J.S. Tan. (2022). The Role of Workers in AI Ethics and Governance. IN: The Oxford Handbook of AI Governance, edited by Justin B. Bullock, Yu-Che Chen, Johannes Himmelreich, Valerie M. Hudson, Anton Korinek, Matthew M. Young, and Baobao Zhang. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780197579329.013.68.
Sioufi, V. (2023, November-December). The Workers AI Hides: Hundreds of Canadian workers are training artificial intelligence for pennies. Briarpatch, 52(6), 22-26. Briarpatch Inc. https://www.proquest.com/magazines/workers-ai-hides/docview/2888647145/se-2?accountid=14511 AND https://link-gale-com.libproxy.ucl.ac.uk/apps/doc/A773244702/AONE?u=ucl_ttda&sid=bookmark-AONE&xid=d31a73ef
Toby Jia-Jun Li, Lu, Y., Clark, J., Chen, M., Cox, V., Jiang, M., Yang, Y., Kay, T., Wood, D., & Brockman, J. (2022). A Bottom-Up End-User Intelligent Assistant Approach to Empower Gig Workers against AI Inequality. arXiv.Org. https://doi.org/10.48550/arxiv.2204.13842
