# Human-in-the-loop Machine Learning: A Macro-Micro Perspective

arXiv: https://arxiv.org/abs/2202.10564 · HTML: https://mlsanity.com/paper/2202.10564/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2022.

## Built on by (most cited first, 4 of 4)

- [Natural Language Processing in the Legal Domain](https://mlsanity.com/paper/2302.12039/) (2023)
- [Pref-GUIDE: Continual Policy Learning from Real-Time Human Feedback via Preference-Based Learning](https://mlsanity.com/paper/2508.07126/) (2025)
- [Semi-Automated Construction of Food Composition Knowledge Base](https://mlsanity.com/paper/2301.11322/) (2023)
- [Human-AI Synergy in Adaptive Active Learning for Continuous Lithium Carbonate Crystallization Optimization](https://mlsanity.com/paper/2507.19316/) (2025)

## Abstract (arXiv)

Though technical advance of artificial intelligence and machine learning has enabled many promising intelligent systems, many computing tasks are still not able to be fully accomplished by machine intelligence. Motivated by the complementary nature of human and machine intelligence, an emerging trend is to involve humans in the loop of machine learning and decision-making. In this paper, we provide a macro-micro review of human-in-the-loop machine learning. We first describe major machine learning challenges which can be addressed by human intervention in the loop. Then we examine closely the latest research and findings of introducing humans into each step of the lifecycle of machine learning. Finally, we analyze current research gaps and point out future research directions.