A Survey of Human-in-the-loop for Machine Learning

By Xingjiao Wu, Luwei Xiao, Yixuan Sun, Junhang Zhang, Tianlong Ma, Liangbo He. Future generations computer systems, 2021

775 citations recorded by the source corpus.

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 major approaches in the field; along with their technical strengths/ weaknesses, we have 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 motivates interested readers to consider approaches for designing effective human-in-the-loop solutions.

Subfields: Artificial Intelligence, Automotive Engineering, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/102ebe229df18c8733ea1b8def56cd79996e2178

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Pengcheng Jiao
2. Luyi Zheng
3. A. Gopinath
4. Noah Shinn
5. Beck Labash
6. Ouyang Fan

In Computer Networks and Communications

1. Pengcheng Jiao
2. Luyi Zheng
3. Ouyang Fan

Citations in the corpus

• Reflexion: an autonomous agent with dynamic memory and self-reflection (2023, background): “To address common failure points, human-in-the-loop (HITL) approaches have been commonly used to improve performance Fan et al. (2022); Wu et al. (2022) Yao et al. (2023) briefly explore a human-in-the-loop (HITL) approach to redirect the agent’s reasoning trace after erroneous actions.”
• Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course (2023, methodology): “The loop includes four steps, namely a) AI model development, b) model and algorithm optimization, c) AI model educational application, and d) validation through empirical research (Wu et al., 2022; Xie et al., 2019; Yang et al., 2021).”