Quantifying the Invisible Labor in Crowd Work
Carlos Toxtli, Siddharth Suri, Saiph Savage
Abstract
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 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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext d79ef665-e3e4-4ee4-94fe-e0ccfa49a6a6Cited by top-tier papers32
- The Future of HCI-Policy CollaborationQian Yang, Richmond Y. Wong, Steven J. Jackson, Sabine Junginger et al.CHI 2024 · 51 citations
- Rethinking "Risk" in Algorithmic Systems Through A Computational Narrative Analysis of Casenotes in Child-WelfareDevansh Saxena, Erina Seh-Young Moon, Aryan Chaurasia, Yixin Guan et al.CHI 2023 · 35 citations
- Designing Gig Worker Sousveillance ToolsKimberly Do, Maya De Los Santos, Michael Muller, Saiph SavageCHI 2024 · 29 citations
- Charting the Automation of Hospitality: An Interdisciplinary Literature Review Examining the Evolution of Frontline Service work in the Face of Algorithmic ManagementFranchesca Spektor, Sarah E. Fox, Ezra Awumey, Ben Begleiter et al.CSCW 2023 · 28 citations
- Making Data Work CountSrravya Chandhiramowuli, Alex S. Taylor, Sara Heitlinger, Ding WangCSCW 2024 · 25 citations
Builds on6
- Improving Worker Engagement Through Conversational Microtask CrowdsourcingSihang Qiu, Ujwal Gadiraju, Alessandro BozzonCHI 2020 · 66 citations
- Becoming the Super Turker: Increasing Wages via a Strategy from High Earning WorkersSaiph Savage, Chun-Wei Chiang, Susumu Saito, Carlos Toxtli et al.WWW 2020 · 53 citations
- CrowdCog: A Cognitive Skill based System for Heterogeneous Task Assignment and Recommendation in CrowdsourcingDanula Hettiachchi, Niels van Berkel, Vassilis Kostakos, Jorge GonçalvesCSCW 2020 · 38 citations
- CrowdCO-OP: Sharing Risks and Rewards in CrowdsourcingShaoyang Fan, Ujwal Gadiraju, Alessandro Checco, Gianluca DemartiniCSCW 2020 · 34 citations
- "I Hope This Is Helpful": Understanding Crowdworkers' Challenges and Motivations for an Image Description TaskRachel N. Simons, Danna Gurari, Kenneth R. FleischmannCSCW 2020 · 27 citations
Related papers
- The Expertise Involved in Deciding which HITs are Worth Doing on Amazon Mechanical TurkBenjamin V. Hanrahan, Anita Chen, Jiahua Ma, Ning F. Ma et al.CSCW 2021 · 16 citations
- Invisible Labor in Open Source Software EcosystemsJohn Meluso, Amanda Casari, Katie McLaughlin, Milo Z. TrujilloCSCW 2025 · 3 citations
- Incorporating Worker Perspectives into MTurk Annotation Practices for NLPOlivia Huang, Eve Fleisig, Dan KleinEMNLP 2023 · 1 citation
- Playing Planning Poker in Crowds: Human Computation of Software Effort EstimatesMohammed Alhamed, Tim StorerICSE 2021 · 18 citations
- Investigating the Accessibility of Crowdwork Tasks on Mechanical TurkStephen Uzor, Jason T. Jacques, John J. Dudley, Per Ola KristenssonCHI 2021 · 27 citations
