The hidden faces of automation
Abstract
As we dream of automation, we always need people to calibrate and train what we automate. Automation has hidden human faces.
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Workers' labor disappears from corporate imaginaries of progress, maintaining the mythology of AI as a technological achievement.
“While the data work industry is rapidly growing and becoming increasingly structured and professionalized, workers’ labor disappears from corporate imaginaries of progress [21, 41, 55, 137], which serves to maintain the mythology of AI as a technological achievement [11, 68, 99].”
From 'The plan is just survival': Data Work in Kenya and the Regime of Entrapment (2026) · cites this paper as [68]
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The Ethics of AI Ethics: An Evaluation of Guidelines
Hagendorff, T. (2020) · Minds and Machines · doi:10.1007/s11023-020-09517-8 · Cited by 1,911 · see citing works
“Issues such as lithium mining, e-waste, the one-way use of rare earth minerals, energy consumption, low-wage “clickworkers” creating labels for data sets or doing content moderation are of relevance here (Crawford and Joler 2018; Irani 2016; Veglis 2014; Fang 2019; Casilli 2017).”
BackgroundPatchwork: The Hidden, Human Labor of AI Integration within Essential Work
Fox, S. E., Shorey, S., Kang, E. Y., Montiel Valle, D., & Rodriguez, E. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579514 · Cited by 68 · see citing works
Hagendorff, T. (2021) · Minds and Machines · doi:10.1007/s11023-021-09573-8 · Cited by 47 · see citing works
Generative AI Considered Harmful
Fischer, J. E. (2023) · Proceedings of the 5th International Conference on Conversational User Interfaces · doi:10.1145/3571884.3603756 · Cited by 47 · see citing works
'The plan is just survival': Data Work in Kenya and the Regime of Entrapment
Kapania, S., Yang, T., Abdelkadir, N. A., Scheuerman, M. K., Miceli, M., Taylor, A. S., & Fox, S. E. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791097 · Cited by 2 · see citing works
“While the data work industry is rapidly growing and becoming increasingly structured and professionalized, workers’ labor disappears from corporate imaginaries of progress [21, 41, 55, 137], which serves to maintain the mythology of AI as a technological achievement [11, 68, 99].”
Stevens, N., & Gieseking, J. J. (2026) · Transactions of the Institute of British Geographers · doi:10.1111/tran.70046 · Cited by 2 · see citing works
- Authors
- Irani, L.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Irani, Lilly
- ORCID
- Irani, Lilly
- Venue
- XRDS: Crossroads, The ACM Magazine for Students
- Published
- 2016-12-15
- Volume
- 23
- Issue
- 2
- Pages
- 34-37
- ISSN
- 1528-4972 (ISSN-L)
- 1528-4980
- OpenAlex
- W2565741774
- Cited by
- 43
- References
- 0
- Citations by year
- 2026: 7 · 2025: 3 · 2024: 2 · 2023: 9 · 2022: 7 · 2021: 6 · 2020: 3 · 2019: 3 · 2018: 2 · 2017: 1
- FWCI
- 4.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- XRDS Crossroads The ACM Magazine for Students (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Keywords
- automation, human factors, human-automation interaction, human oversight, training, calibration
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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