Humans in the AI loop: the data labelers behind some of the most powerful LLMs' training datasets

High quality training data, particularly labeled datasets, are essential for creating effective large language models (LLMs), yet the system relies heavily on exploited workers.

Workers face a lack of transparency regarding who their data is helping to serve, and they navigate uncertain job security and unpredictable earnings under algorithm-driven pressures.

Data labeling forms a crucial part of the AI supply chain; however, the exploitative nature of this labor highlights a significant ethical concern within the industry.

As AI developers rush to scale up LLM training, the dependency on invisible, underpaid data labelers raises questions about the sustainability and ethical implications of this approach.

Read at Privacy International