A survey of human-in-the-loop for machine learning

Machine learning has become the state-of-the-art technique for many tasks including computer vision, natural language processing, speech processing tasks, etc. However, the unique challenges posed by machine learning suggest that incorporating user knowledge into the system can be beneficial. The purpose of integrating human domain knowledge is also to promote the automation of machine learning. Human-in-the-loop is an area that we see as increasingly important in future research due to the knowledge learned by machine learning cannot win human domain knowledge. Human-in-the-loop aims to train an accurate prediction model with minimum cost by integrating human knowledge and experience. Humans can provide training data for machine learning applications and directly accomplish tasks that are hard for computers in the pipeline with the help of machine-based approaches. In this paper, we survey existing works on human-in-the-loop from a data perspective and classify them into three categories with a progressive relationship: (1) the work of improving model performance from data processing, (2) the work of improving model performance through interventional model training, and (3) the design of the system independent human-in-the-loop. Using the above categorization, we summarize the major approaches in the field; along with their technical strengths/weaknesses, we have a simple classification and discussion in natural language processing, computer vision, and others. Besides, we provide some open challenges and opportunities. This survey intends to provide a high-level summarization for human-in-the-loop and to motivate interested readers to consider approaches for designing effective human-in-the-loop solutions.