Leveraging LLMs for Fair Data Labeling and Validation in Crowdsourcing Environments [Vision Paper]

The rapid expansion of large datasets, encompassing text, images, audio, and video, presents substantial challenges for data labeling, a crucial step in machine learning and data science workflows. Large Language Models (LLMs) provide a promising approach for automating and improving the accuracy of data labeling across various modalities. However, their application in this area introduces specific challenges, such as managing diverse data types, maintaining high-quality annotations, handling computational complexity, and, critically, addressing and mitigating biases associated with automated methods. This vision paper examines the potential of LLMs to enable fair data labeling and validation in crowdsourcing environments, discussing the current landscape, and existing challenges, providing potential future research directions that can help ensure their successful integration.