While large language models (LLMs) show promise in hospitality and tourism, their ability to provide unbiased service across demographic groups remains unclear. This paper explores gender and ethnic biases when LLMs are utilized as travel planning assistants. To investigate this issue, we apply machine learning techniques to analyze travel suggestions generated from three open-source LLMs. Our findings reveal that the performance of race and gender classifiers substantially exceeds random chance, indicating differences in how LLMs engage with varied subgroups. Specifically, outputs align with cultural expectations tied to certain races and genders. To minimize the effect of these stereotypes, we used a stop-word classification strategy, which decreased identifiable differences, with no disrespectful terms found. However, hallucinations related to African American and gender minority groups were noted. In conclusion, while LLMs can generate travel plans seemingly free from bias, it remains essential to verify the accuracy and appropriateness of their recommendations.

The authors provide a crisp yet in-depth summary of the relevance of artificial intelligence in the hospitality and tourism industry. Focusing on artificial intelligence, the chapter draws the attention of the reader toward its usage and role in the lives of the customers and service providers in the hospitality sector. It also focuses on the costs involved in the incorporation of artificial intelligence in the hospitality industry. Some of the salient features of artificial intelligence in the hospitality sector have been discussed in the chapter by elucidating the benefits of artificial intelligence like financial benefits, technological benefits, and resource management benefits. This chapter sought to project the various factors affecting the role of artificial intelligence in the industry and how the hospitality and tourism industry adopts the technology with time, demand, and expectations of the customer. Both perspectives have been highlighted, i.e., from the customer's point of view as well as the managerial insights.