Quantifying the Invisible Labor in Crowd Work
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Abstract (arXiv)
Crowdsourcing markets provide workers with a centralized place to find paid work. What may not be obvious at first glance is that, in addition to the work they do for pay, crowd workers also have to shoulder a variety of unpaid invisible labor in these markets, which ultimately reduces workers' hourly wages. Invisible labor includes finding good tasks, messaging requesters, or managing payments. However, we currently know little about how much time crowd workers actually spend on invisible labor or how much it costs them economically. To ensure a fair and equitable future for crowd work, we need to be certain that workers are being paid fairly for all of the work they do. In this paper, we conduct a field study to quantify the invisible labor in crowd work. We build a plugin to record the amount of time that 100 workers on Amazon Mechanical Turk dedicate to invisible labor while completing 40,903 tasks. If we ignore the time workers spent on invisible labor, workers' median hourly wage was 3.76. But, we estimated that crowd workers in our study spent 33% of their time daily on invisible labor, dropping their median hourly wage to 2.83. We found that the invisible labor differentially impacts workers depending on their skill level and workers' demographics. The invisible labor category that took the most time and that was also the most common revolved around workers having to manage their payments. The second most time-consuming invisible labor category involved hyper-vigilance, where workers vigilantly watched over requesters' profiles for newly posted work or vigilantly searched for labor. We hope that through our paper, the invisible labor in crowdsourcing becomes more visible, and our results help to reveal the larger implications of the continuing invisibility of labor in crowdsourcing.
Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Oct 2021.
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Citing papers per year: 2022: 1; 2023: 4; 2024: 5; 2025: 3; 2026: 3.
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- 2024Red-Teaming for Generative AI: Silver Bullet or Security Theater?cited by 81
- 2022CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotationcited by 59
- 2023Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generationcited by 55
- 2023Ethical Considerations for Responsible Data Curationcited by 17
- 2024Why Does ChatGPT "Delve" So Much? Exploring the Sources of Lexical Overrepresentation in Large Language Modelscited by 8
- 2025Fairshare Data Pricing via Data Valuation for Large Language Modelscited by 7
- 2023The ethical ambiguity of AI data enrichment: Measuring gaps in research ethics norms and practicescited by 3
- 2023Making Data Work Countcited by 2
- 2024A Taxonomy of Challenges to Curating Fair Datasetscited by 2
- 2025Word Overuse and Alignment in Large Language Models: The Influence of Learning from Human Feedbackcited by 2
- 2024Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Modelscited by 1
- 2026The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotationcited by 1
4 more
- 2024A Culturally-Aware Tool for Crowdworkers: Leveraging Chronemics to Support Diverse Work Stylescited by 0
- 2025Data Enrichment Work and AI Labor in Latin America and the Caribbeancited by 0
- 2026Upskilling with Generative AI: Practices and Challenges for Freelance Knowledge Workerscited by 0
- 2026How Formerly Incarcerated People Envision Technologies for Prison Parolecited by 0
- 2026How Formerly Incarcerated People Envision Technologies for Prison Parole17 Jul 2026
- 2026Upskilling with Generative AI: Practices and Challenges for Freelance Knowledge Workers29 Apr 2026
- 2026The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation11 Feb 2026
- 2025Word Overuse and Alignment in Large Language Models: The Influence of Learning from Human Feedback3 Aug 2025
- 2025Fairshare Data Pricing via Data Valuation for Large Language Models31 Jan 2025
- 2025Data Enrichment Work and AI Labor in Latin America and the Caribbean13 Jan 2025
- 2024Why Does ChatGPT "Delve" So Much? Exploring the Sources of Lexical Overrepresentation in Large Language Models16 Dec 2024
- 2024Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Models17 Oct 2024
- 2024A Culturally-Aware Tool for Crowdworkers: Leveraging Chronemics to Support Diverse Work Styles31 Jul 2024
- 2024A Taxonomy of Challenges to Curating Fair Datasets10 Jun 2024
- 2024Red-Teaming for Generative AI: Silver Bullet or Security Theater?29 Jan 2024
- 2023Making Data Work Count29 Nov 2023
4 more
- 2023The ethical ambiguity of AI data enrichment: Measuring gaps in research ethics norms and practices1 Jun 2023
- 2023Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation1 May 2023
- 2023Ethical Considerations for Responsible Data Curation7 Feb 2023
- 2022CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation9 Jun 2022
- 2023Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generationcited by 55
- 2024Why Does ChatGPT "Delve" So Much? Exploring the Sources of Lexical Overrepresentation in Large Language Modelscited by 8
- 2025Word Overuse and Alignment in Large Language Models: The Influence of Learning from Human Feedbackcited by 2
- 2025Word Overuse and Alignment in Large Language Models: The Influence of Learning from Human Feedback3 Aug 2025
- 2024Why Does ChatGPT "Delve" So Much? Exploring the Sources of Lexical Overrepresentation in Large Language Models16 Dec 2024
- 2023Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation1 May 2023
- 2022CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotationcited by 59
- 2023Ethical Considerations for Responsible Data Curationcited by 17
- 2025Fairshare Data Pricing via Data Valuation for Large Language Modelscited by 7
- 2023The ethical ambiguity of AI data enrichment: Measuring gaps in research ethics norms and practicescited by 3
- 2023Making Data Work Countcited by 2
- 2024A Taxonomy of Challenges to Curating Fair Datasetscited by 2
- 2024Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Modelscited by 1
- 2026The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotationcited by 1
- 2024A Culturally-Aware Tool for Crowdworkers: Leveraging Chronemics to Support Diverse Work Stylescited by 0
- 2025Data Enrichment Work and AI Labor in Latin America and the Caribbeancited by 0
- 2026Upskilling with Generative AI: Practices and Challenges for Freelance Knowledge Workerscited by 0
- 2026How Formerly Incarcerated People Envision Technologies for Prison Parolecited by 0
- 2026How Formerly Incarcerated People Envision Technologies for Prison Parole17 Jul 2026
- 2026Upskilling with Generative AI: Practices and Challenges for Freelance Knowledge Workers29 Apr 2026
- 2026The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation11 Feb 2026
- 2025Fairshare Data Pricing via Data Valuation for Large Language Models31 Jan 2025
- 2025Data Enrichment Work and AI Labor in Latin America and the Caribbean13 Jan 2025
- 2024Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Models17 Oct 2024
- 2024A Culturally-Aware Tool for Crowdworkers: Leveraging Chronemics to Support Diverse Work Styles31 Jul 2024
- 2024A Taxonomy of Challenges to Curating Fair Datasets10 Jun 2024
- 2023Making Data Work Count29 Nov 2023
- 2023The ethical ambiguity of AI data enrichment: Measuring gaps in research ethics norms and practices1 Jun 2023
- 2023Ethical Considerations for Responsible Data Curation7 Feb 2023
- 2022CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation9 Jun 2022