# Exploring Human-in-the-Loop Themes in AI Application Development: An Empirical Thematic Analysis

arXiv: https://arxiv.org/abs/2603.05510 · HTML: https://mlsanity.com/paper/2603.05510/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 39 references that are in the map.
- First posted on arXiv 18 Jan 2026.

## Builds on (most cited first, 18 of 18)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [What Prompts Don't Say: Understanding and Managing Underspecification in LLM Prompts](https://mlsanity.com/paper/2505.13360/) (2025)
- [LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey](https://mlsanity.com/paper/2505.00753/) (2025)
- [Human-In-The-Loop Person Re-Identification](https://mlsanity.com/paper/1612.01345/) (2016)
- [Efficient, Uncertainty-based Moderation of Neural Networks Text Classifiers](https://mlsanity.com/paper/2204.01334/) (2022)
- [Design Patterns for Machine Learning Based Systems with Human-in-the-Loop](https://mlsanity.com/paper/2312.00582/) (2023)
- [AI and Human Oversight: A Risk-Based Framework for Alignment](https://mlsanity.com/paper/2510.09090/) (2025)
- [An HCAI Methodological Framework (HCAI-MF): Putting It Into Action to Enable Human-Centered AI](https://mlsanity.com/paper/2311.16027/) (2023)
- [Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition](https://mlsanity.com/paper/2505.06325/) (2025)

## Abstract (arXiv)

Developing and deploying AI applications in organizations is challenging when human decision authority and oversight are underspecified across the system lifecycle. Although Human-in-the-Loop (HITL) and Human-Centered AI (HCAI) principles are widely acknowledged, operational guidance for structuring roles, checkpoints, and feedback mechanisms remains fragmented. We report a multi-source qualitative study: a retrospective diary study of a customer-support chatbot and semi-structured interviews with eight AI experts from academia and industry. Through five-cycle thematic analysis of 1,435 codewords, we derive four themes: AI Governance and Human Authority, Human-in-the-Loop Iterative Refinement, AI System Lifecycle and Operational Constraints, and Human-AI Team Collaboration and Coordination. These themes provide empirical inputs for subsequent HITL framework design and validation.