Managing the Human in the Loop

Active ML promises more efficient ML by intelligently selecting the most informative samples for labeling by human oracles. However, the success of these human-in-the-loop systems depends on effective interface design and workflow management. In this chapter, we will cover best practices for optimizing the human role in active ML. First, we will explore interactive system design, discussing how to create labeling interfaces that enable efficient and accurate annotations. Next, we will provide an extensive overview of the leading human-in-the-loop frameworks for managing the labeling pipeline. We will then turn to handling model-label disagreements through adjudication and quality control. After that, we will discuss strategies for recruiting qualified labelers and managing them effectively. Finally, we will examine techniques for evaluating and ensuring high-quality annotations and properly balanced datasets. By the end of this chapter, you will have...