Summary
Throughout this chapter, we’ve examined the critical role that humans play in ensuring data quality, particularly in the initial stages of data labeling. We’ve recognized that while human labelers are indispensable, they also present certain challenges, including biases and inconsistencies.
To address these issues, we’ve explored various strategies to train labelers effectively for high-quality dataset development. The key takeaway here is that well-trained labelers, armed with clear instructions, can significantly increase the overall quality of your data.
Improving task instructions emerged as a recurring theme, underscoring their importance in facilitating the labeling process. Iterative collaboration was also highlighted as an essential practice, promoting continuous improvement through feedback and refinement.
By the end of this chapter, you should have gained a comprehensive understanding of why human involvement is crucial in data-centric...
