11 Interfaces for data annotation

 

This chapter covers

  • Understanding basic human–computer interaction principles
  • Applying human–computer interaction principles in annotation interfaces
  • Combining human and machine intelligence to maximize the strengths of each
  • Implementing interfaces with different levels of machine learning integration
  • Adding machine learning to applications without disrupting existing work practices

In the past 10 chapters, we have covered everything about human-in-the-loop machine learning except the vital component of the human-machine interface. This chapter covers how to build interfaces that maximize the efficiency and accuracy of the annotations. This chapter also covers the trade-offs: there is no one set of interface conventions that can be applied to every task, so you must make an informed decision about what is the best user experience for your task and your annotators.

11.1 Basic principles of human–computer interaction

11.1.1 Introducing affordance, feedback, and agency

11.1.2 Designing interfaces for annotation

11.1.3 Minimizing eye movement and scrolling

11.1.4 Keyboard shortcuts and input devices

11.2 Breaking the rules effectively

11.2.1 Scrolling for batch annotation

11.2.2 Foot pedals

11.2.3 Audio inputs

11.3 Priming in annotation interfaces

11.3.1 Repetition priming

11.3.2 Where priming hurts