# Human-in-the-loop Artificial Intelligence

arXiv: https://arxiv.org/abs/1710.08191 · HTML: https://mlsanity.com/paper/1710.08191/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2017.

## Built on by (most cited first, 30 of 40)

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- [Intelligent AI Delegation](https://mlsanity.com/paper/2602.11865/) (2026)
- [XAIR: A Framework of Explainable AI in Augmented Reality](https://mlsanity.com/paper/2303.16292/) (2023)
- [Make The Most of Prior Data: A Solution for Interactive Text Summarization with Preference Feedback](https://mlsanity.com/paper/2204.05512/) (2022)
- [Monitored Markov Decision Processes](https://mlsanity.com/paper/2402.06819/) (2024)
- [On the Importance of Domain-specific Explanations in AI-based Cybersecurity Systems (Technical Report)](https://mlsanity.com/paper/2108.02006/) (2021)
- [SCALE: Towards Collaborative Content Analysis in Social Science with Large Language Model Agents and Human Intervention](https://mlsanity.com/paper/2502.10937/) (2025)
- [Tight Performance Guarantees of Imitator Policies with Continuous Actions](https://mlsanity.com/paper/2212.03922/) (2022)
- [ColorBrowserAgent: Complex Long-Horizon Browser Agent with Adaptive Knowledge Evolution](https://mlsanity.com/paper/2601.07262/) (2026)
- [A-Eye: Driving with the Eyes of AI for Corner Case Generation](https://mlsanity.com/paper/2202.10803/) (2022)
- [Human Delegation Behavior in Human-AI Collaboration: The Effect of Contextual Information](https://mlsanity.com/paper/2401.04729/) (2024)
- [Machine learning in business process management: A systematic literature review](https://mlsanity.com/paper/2405.16396/) (2024)
- [Manifestations of Xenophobia in AI Systems](https://mlsanity.com/paper/2212.07877/) (2022)
- [Artificial Intelligence Studies in Cartography: A Review and Synthesis of Methods, Applications, and Ethics](https://mlsanity.com/paper/2312.07901/) (2023)
- [World Models in Artificial Intelligence: Sensing, Learning, and Reasoning Like a Child](https://mlsanity.com/paper/2503.15168/) (2025)
- [Toward Safe and Responsible AI Agents: A Three-Pillar Model for Transparency, Accountability, and Trustworthiness](https://mlsanity.com/paper/2601.06223/) (2026)
- [Ensemble of Convolution Neural Networks on Heterogeneous Signals for Sleep Stage Scoring](https://mlsanity.com/paper/2107.11045/) (2021)
- [Humans Coexist, So Must Embodied Artificial Agents](https://mlsanity.com/paper/2502.04809/) (2025)
- [Reduced AI Acceptance After the Generative AI Boom: Evidence From a Two-Wave Survey Study](https://mlsanity.com/paper/2510.23578/) (2025)
- ["It is there, and you need it, so why do you not use it?" Achieving better adoption of AI systems by domain experts, in the case study of natural science research](https://mlsanity.com/paper/2403.16895/) (2024)
- [Automatic explanation of the classification of Spanish legal judgments in jurisdiction-dependent law categories with tree estimators](https://mlsanity.com/paper/2404.00437/) (2024)
- [Deployability-Centric Infrastructure-as-Code Generation: Fail, Learn, Refine, and Succeed through LLM-Empowered DevOps Simulation](https://mlsanity.com/paper/2506.05623/) (2025)
- [A Human-in/on-the-Loop Framework for Accessible Text Generation](https://mlsanity.com/paper/2603.18879/) (2026)
- [ShopEase: A Generative AI-Based Multi-Agent Framework for Intelligent Enterprise Customer Support Using Hybrid Retrieval-Augmented Generation](https://mlsanity.com/paper/2609.13856/) (2026)
- [Power of human-algorithm collaboration in solving combinatorial optimization problems](https://mlsanity.com/paper/2107.11784/) (2021)
- [Demonstrating REACT: a Real-time Educational AI-powered Classroom Tool](https://mlsanity.com/paper/2108.07693/) (2021)

## Abstract (arXiv)

Little by little, newspapers are revealing the bright future that Artificial Intelligence (AI) is building. Intelligent machines will help everywhere. However, this bright future has a dark side: a dramatic job market contraction before its unpredictable transformation. Hence, in a near future, large numbers of job seekers will need financial support while catching up with these novel unpredictable jobs. This possible job market crisis has an antidote inside. In fact, the rise of AI is sustained by the biggest knowledge theft of the recent years. Learning AI machines are extracting knowledge from unaware skilled or unskilled workers by analyzing their interactions. By passionately doing their jobs, these workers are digging their own graves. In this paper, we propose Human-in-the-loop Artificial Intelligence (HIT-AI) as a fairer paradigm for Artificial Intelligence systems. HIT-AI will reward aware and unaware knowledge producers with a different scheme: decisions of AI systems generating revenues will repay the legitimate owners of the knowledge used for taking those decisions. As modern Robin Hoods, HIT-AI researchers should fight for a fairer Artificial Intelligence that gives back what it steals.