Amazon Mechanical Turk
By Francesco Cervellini. 2018
525 citations recorded by the source corpus.
Subfields: Management of Technology and Innovation
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
Citations in the corpus
- A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, background): “One of the earliest and most popular platforms is Amazon Mechanical Turk [61] where tasks (called HITs ) are assigned to human workers, and workers are compensated for finishing the tasks.”
- A Survey of App Store Analysis for Software Engineering (2017, background): “study usingAmazon’sMechanical Turk [7], on a set of 100 apps, and asked whether the generated descriptions were readable and whether they could reduce the rate at which users download malware.”
- Visual Entailment: A Novel Task for Fine-Grained Image Understanding (2019, unknown): “The authors of SNLI collect multiple hypotheses in the three classes - entailment, neutral, and contradiction - for a given premise via Amazon Mechanical Turk [68], resulting in about 570K (Ptext, Htext) pairs.”
- FeatureHub: Towards Collaborative Data Science (2017, background): “Crowdsourced data labeling : More and more researchers have used the crowd to label datasets, often of images, at scale, using crowdsourcing platforms like Amazon Mechanical Turk [9]–[11].”
- The quality of data collected online: An investigation of careless responding in a crowdsourced sample (2020, background): “online studies, and the possible influence of distractions in an uncontrolled setting, data collected online may suffer from bad quality stemming from carelessness or different forms of deceptive behavior (Cheung et al., 2017; Fleischer et al., 2015).”
- Aligning Large Multimodal Models with Factually Augmented RLHF (2023, methodology): “Labeler Information We hired 28 anonymized labelers from the Amazon Mechanical Turk (Turk, 2012) platform.”