Whose hotel does the AI recommend? An algorithm audit of reputation signals in LLM-assisted hotel selection
arXiv:2606.16344v1 Announce Type: cross Abstract: Travelers increasingly ask large language model (LLM) assistants which hotel to book, making these systems gatekeepers of property visibility -- yet what moves their recommendations is undocumented. We conduct a pre-specified algorithm audit using a randomized choice-based conjoint: across personas, prompt templates, and twelve open-weight and proprietary models, assistants choose among five hotels whose guest rating, review volume and recency, management response, chain affiliation, price, eco-certification, and list position are independently
The proliferation of LLMs into consumer-facing applications, particularly in recommendation systems, necessitates immediate scrutiny into their operational mechanisms and potential biases.
This research provides crucial insights into how LLMs prioritize factors in recommendations, directly impacting consumer choice, market dynamics, and the fairness of digital gatekeepers.
Understanding the undocumented 'reputation signals' within LLM recommendations allows for better audits, potentially leading to fairer algorithms and more transparent booking processes.
- · Ethical AI developers
- · Open-source LLM audit tools
- · Consumers seeking unbiased recommendations
- · LLM developers with opaque algorithms
- · Hotels relying on gaming reputation signals
- · Proprietary recommendation systems
Increased pressure on LLM providers to disclose or explain their recommendation mechanisms and underlying biases.
Development of regulatory frameworks or industry standards specifically for AI-driven recommendation transparency and accountability.
A shift in consumer behavior where users demand and seek out 'transparent' or 'audited' AI recommendation systems, similar to eco-certifications for products.
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Read at arXiv cs.CL