What is Human-in-the-Loop?

Human-in-the-Loop (HITL) is an approach to machine learning and artificial intelligence that involves humans in the development, training, and evaluation process. This approach is particularly used when dealing with complex tasks or when the model’s output requires a high level of accuracy. HITL can involve humans in various stages of the machine learning pipeline, such as data labeling, model validation, and active learning. By incorporating human expertise, HITL aims to improve the overall performance and interpretability of AI systems.

Benefits of Human-in-the-Loop

  • Improved model performance: Human expertise can help identify and correct errors in model predictions.

  • Better interpretability: Human feedback helps make AI systems more understandable and transparent.

  • Active learning: Including humans in the loop can help prioritize data points for model training and improve the learning process.

Resources on Human-in-the-Loop