Assessing the Influence of Feedback Strategies on Errors in Crowdsourced Annotation of Tumor Images
Year: 2025
Type: article
Abstract: Crowdsourcing enables the acquisition of distributed human intelligence for solving tasks involving human judgments in scalable ways, with many use cases in various application areas accessing human intelligence. However, crowdworkers completing the tasks may have limited or no background knowledge about the tasks they solve due to the plethora of various tasks available. Therefore, the tasks—even on a micro scale—also need to include appropriate training for the crowdworkers to enable them to complete them successfully. However, training crowdworkers efficiently in a short time for complex tasks poses a challenge and remains an unresolved issue. This paper addresses this challenge by empirically comparing different training strategies for crowdworkers and evaluating their impact on the crowdworkers’ task results. We perform comparisons between a basic training strategy, a strategy based on previous errors made by other crowdworkers, and the addition of instant feedback during training and task completion. Our results show that adding instant feedback during both the training phase and during the task yields more attention from the workers in difficult tasks and hence reduces errors and improves the results. We conclude that more attention is retained when the content of instant feedback includes information about mistakes made by other crowdworkers previously.
Source: Big Data and Cognitive Computing
DOI: 10.3390/bdcc9090220
Institutions: Technische Universität Ilmenau, University of Würzburg
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W4413640844