Understanding the benefits of diverse human labeling
Incorporating a diverse range of individuals and perspectives in the human labeling process offers several advantages. Humans bring a level of precision and accuracy to data annotation that is difficult for machines to match. While automated systems may struggle with ambiguity or complexity, human annotators can leverage their understanding and reasoning capabilities to make informed decisions.
Data can change over time, and new scenarios can arise that were not present in the original training data. Human annotators can adapt to these changes, providing updated annotations that reflect the new realities. This ensures that ML models remain relevant and effective as the data evolves.
Some key strengths of human labelers over programmatic labeling include the following:
- Domain expertise: Labelers with subject-matter expertise can provide valuable insights and annotations that help the model better comprehend specific...
