A Predictive Framework for Failure Detection and Reallocation in Crowdsourcing Platforms

Crowdsourcing platforms have become essential tools for managing various tasks, relying on distributed labour practices to enhance productivity and goal achievement. However, they often encounter challenges such as unemployment and low-quality task submissions, impeding project effectiveness. Timely resolution of these issues is crucial for project momentum. To address these challenges, we propose a predictive model based on machine learning techniques. Our framework aims to anticipate mistakes and optimize workload reallocation within community spaces. Leveraging state-of-the-art algorithms, our system analyzes employee behaviours, task features, and platform dynamics to detect problematic situations early. Moreover, we introduce a dynamic reallocation technique to expedite project duration while minimizing delays. This method automatically reassigns tasks to alternative tasks. The experimentation results exhibit that our framework has shown that it is better than other methods at accurately detecting failures and reassigning tasks. Furthermore, our framework’s adaptability enables its application across various crowd-sourcing fields, including data annotation and content moderation. Described as a proactive response to failures within crowd-sourcing platforms, our predictive framework offers a promising solution to enhance project efficiency.