Summary
This chapter explored strategies for effectively incorporating human input into active ML systems. We discussed how to design workflows that enable efficient collaboration between humans and AI models. Leading open source frameworks for human-in-the-loop learning were reviewed, including their capabilities for annotation, verification, and active learning.
Handling model-label disagreements is a key challenge in human-AI systems. Techniques such as manually reviewing conflicts and active learning cycles help identify and resolve mismatches. Carefully managing the human annotation workforce is also critical as it covers recruiters, training, quality control, and tooling.
A major focus was ensuring high-quality balanced datasets while using methods such as qualification exams, inter-annotator metrics such as the accuracy or the Kappa score, consensus evaluations, and targeted sampling. By implementing robust processes around collaboration, conflict resolution, annotator...
