# A Glimpse Far into the Future: Understanding Long-term Crowd Worker Quality

arXiv: https://arxiv.org/abs/1609.04855 · HTML: https://mlsanity.com/paper/1609.04855/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2016.

## Built on by (most cited first, 5 of 5)

- [Ethical Considerations for Responsible Data Curation](https://mlsanity.com/paper/2302.03629/) (2023)
- [Designing LLM Chains by Adapting Techniques from Crowdsourcing Workflows](https://mlsanity.com/paper/2312.11681/) (2023)
- [Sketch-based Creativity Support Tools using Deep Learning](https://mlsanity.com/paper/2111.09991/) (2021)
- [How Annotation Trains Annotators: Competence Development in Social Influence Recognition](https://mlsanity.com/paper/2604.02951/) (2026)
- [Visual Intelligence through Human Interaction](https://mlsanity.com/paper/2111.06913/) (2021)

## Abstract (arXiv)

Microtask crowdsourcing is increasingly critical to the creation of extremely large datasets. As a result, crowd workers spend weeks or months repeating the exact same tasks, making it necessary to understand their behavior over these long periods of time. We utilize three large, longitudinal datasets of nine million annotations collected from Amazon Mechanical Turk to examine claims that workers fatigue or satisfice over these long periods, producing lower quality work. We find that, contrary to these claims, workers are extremely stable in their quality over the entire period. To understand whether workers set their quality based on the task's requirements for acceptance, we then perform an experiment where we vary the required quality for a large crowdsourcing task. Workers did not adjust their quality based on the acceptance threshold: workers who were above the threshold continued working at their usual quality level, and workers below the threshold self-selected themselves out of the task. Capitalizing on this consistency, we demonstrate that it is possible to predict workers' long-term quality using just a glimpse of their quality on the first five tasks.