The Hidden Cost of Using Amazon Mechanical Turk for Research
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Abstract (arXiv)
In this study, we investigate the attentiveness exhibited by participants sourced through Amazon Mechanical Turk (MTurk), thereby discovering a significant level of inattentiveness amongst the platform's top crowd workers (those classified as 'Master', with an 'Approval Rate' of 98% or more, and a 'Number of HITS approved' value of 1,000 or more). A total of 564 individuals from the United States participated in our experiment. They were asked to read a vignette outlining one of four hypothetical technology products and then complete a related survey. Three forms of attention check (logic, honesty, and time) were used to assess attentiveness. Through this experiment we determined that a total of 126 (22.3%) participants failed at least one of the three forms of attention check, with most (94) failing the honesty check - followed by the logic check (31), and the time check (27). Thus, we established that significant levels of inattentiveness exist even among the most elite MTurk workers. The study concludes by reaffirming the need for multiple forms of carefully crafted attention checks, irrespective of whether participant quality is presumed to be high according to MTurk criteria such as 'Master', 'Approval Rate', and 'Number of HITS approved'. Furthermore, we propose that researchers adjust their proposals to account for the effort and costs required to address participant inattentiveness.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jan 2021.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 2; 2026: 1.
6 papers.
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- 2026Looks Can be Deceiving: Annotator and Reviewer Performance Across Imagery Sources in Crowd-Sourced Aerial Damage Assessmentcited by 0
- 2026Looks Can be Deceiving: Annotator and Reviewer Performance Across Imagery Sources in Crowd-Sourced Aerial Damage Assessment14 Aug 2026
- 2025Streaming, Fast and Slow: Cognitive Load-Aware Streaming for Efficient LLM Serving25 Apr 2025
- 2025Improving User Behavior Prediction: Leveraging Annotator Metadata in Supervised Machine Learning Models26 Mar 2025
- 2024HAIFAI: Human-AI Interaction for Mental Face Reconstruction9 Dec 2024
- 2024Repairs in a Block World: A New Benchmark for Handling User Corrections with Multi-Modal Language Models21 Sep 2024
- 2022Detecting Label Errors by using Pre-Trained Language Models25 May 2022