Crowdsourcing Model

Crowdsourcing models leverage the collective intelligence of many individuals to generate large datasets for machine learning, but inherent label noise from diverse annotators poses a significant challenge. Current research focuses on developing robust methods to mitigate this noise, employing techniques ranging from classical statistical models and matrix factorization to deep learning approaches, often incorporating signal processing principles. These advancements are crucial for improving the accuracy and reliability of machine learning models across various applications, from combating fake news to enhancing the fairness of algorithms, and are actively being explored in real-time applications and human-in-the-loop systems.