Human-in-the-Loop Contextual Bandits for Short-Term Rental Dynamic Pricing: Structural Equivalence of Historical Warm-Up and Approval-Gated Live Learning
arXiv:2606.02595v1 Announce Type: new Abstract: Dynamic pricing in short-term rental (STR) markets presents a distinctive challenge for online learning algorithms: pricing decisions carry significant financial risk, operators require explainability, and market feedback is sparse (one booking outcome per listed night). We introduce the Human-in-the-Loop Gated Bandit (HITL-GB) framework, in which a contextual bandit algorithm generates price recommendations but a human agent retains authority to accept, modify, or reject each recommendation before it is applied. We show that under this approval
The increasing sophistication of AI algorithms coupled with the need for reliability and explainability in high-stakes economic decisions, like dynamic pricing, drives the development of human-in-the-loop systems.
This development allows for the deployment of AI in financially risky real-world scenarios while maintaining human oversight and accountability, bridging the gap between algorithmic efficiency and practical application.
Previously theoretical or fully autonomous AI pricing models can now be safely implemented in short-term rental markets through a human-supervised approval process.
- · Short-term rental operators
- · AI algorithm developers (contextual bandits)
- · Hospitality tech platforms
- · Data scientists focused on applied AI
- · Platforms without robust human-in-the-loop mechanisms
More widespread adoption of AI-driven dynamic pricing in various industries with high financial risk and human oversight requirements.
Increased demand for tools and interfaces that facilitate effective human-AI collaboration and approval workflows.
Potential for new regulatory frameworks specifically addressing the liabilities and responsibilities in human-in-the-loop AI systems.
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Read at arXiv cs.LG