Keyword

crowd workers

Description: People who perform paid or unpaid tasks distributed through online crowdsourcing platforms, often contributing human labor to digital services or research.

Primary topic: Mobile Crowdsensing and Crowdsourcing (31%)

OpenAlex ID: keywords/crowd-workers

ChatGPT outperforms crowd workers for text-annotation tasks

2023 · Fabrizio Gilardi, Meysam Alizadeh, et al. · Proceedings of the National Academy of Sciences · 1,178

Who are the crowdworkers?

2010 · Joel Ross, Lilly Irani, et al. · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · 1,004

Being a turker

2014 · David Martin, Benjamin V. Hanrahan, et al. · ACM Conference on Computer-Supported Cooperative Work & Social Computing (CSCW) · 279

Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation

2021 · Markus Freitag, George Foster, et al. · Transactions of the Association for Computational Linguistics · 267

Gender differences in altruism on Mechanical Turk: Expectations and actual behaviour

2018 · Pablo Brañas-Garza, Valerio Capraro, et al. · Economics Letters · 226

ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning

2023 · Törnberg, Petter · arXiv (Cornell University) · 151

PDF

Chorus

2013 · Walter S. Lasecki, Rachel Wesley, et al. · ACM Symposium on User Interface Software and Technology (UIST) · 148

Taking a HIT

2016 · Brian McInnis, Dan Cosley, et al. · CHI Conference on Human Factors in Computing Systems · 119

PDF

How Different Groups Prioritize Ethical Values for Responsible AI

2022 · Maurice Jakesch, Zana Buçinca, et al. · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · 116

PDF

Microphones, not megaphones: Functional crowdworker voice regimes on digital work platforms

2020 · Thomas Gegenhuber, Markus Ellmer, et al. · Human Relations · 110

Responsible research with crowds

2018 · M. S. Silberman, B. Tomlinson, et al. · Communications of the ACM · 109

Labelling instructions matter in biomedical image analysis

2023 · Tim Rädsch, Annika Reinke, et al. · Nature Machine Intelligence · 89

PDF

Designing Ground Truth and the Social Life of Labels

2021 · Michael Muller, Christine T. Wolf, et al. · CHI Conference on Human Factors in Computing Systems · 83

Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Prompting

2023 · Hai Dang, Sven Goller, et al. · CHI Conference on Human Factors in Computing Systems · 82

PDF

Supporting Workplace Detachment and Reattachment with Conversational Intelligence

2018 · Alex C. Williams, Harmanpreet Kaur, et al. · CHI Conference on Human Factors in Computing Systems · 82

Mechanical Turk is Not Anonymous

2013 · Matthew Lease, Jessica Hullman, et al. · SSRN Electronic Journal · 82

Zensors

2015 · Gierad Laput, Walter S. Lasecki, et al. · 81

Incentives and rewarding in social computing

2013 · Ognjen Scekic, Hong-Linh Truong, et al. · Communications of the ACM · 78

No Workflow Can Ever Be Enough

2017 · Daniela Retelny, Michael S. Bernstein, et al. · Proceedings of the ACM on Human-Computer Interaction · 77

PDF

Combining Crowd and Expert Labels Using Decision Theoretic Active Learning

2015 · An Nguyen, Byron Wallace, et al. · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · 74

PDF

Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks

2023 · Veselovsky, Veniamin, Ribeiro, Manoel Horta, et al. · arXiv (Cornell University) · 73

PDF

Look before you leap: Legal pitfalls of crowdsourcing

2011 · Stephen M. Wolfson, Matthew Lease · Proceedings of the American Society for Information Science and Technology · 73

Motivation and sustained participation in the online crowdsourcing community: the moderating role of community commitment

2020 · Wei Wu, Xiang Gong · Internet Research · 72

Answering visual questions with conversational crowd assistants

2013 · Walter S. Lasecki, Phyo Thiha, et al. · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · 70

PDF

Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation

2021 · Bartolo, Max, Thrush, Tristan, et al. · arXiv (Cornell University) · 69

PDF

Works count: 528

Citation count: 8,149