Examination of Ethical Principles for LLM-Based Recommendations in Conversational AI

Abstract

Conversational interfaces allow users to experience artificial intelligence (AI) services through text or voice conversations. One common form of a conversational interface is a chatbot, which can be scenario-based or large language model (LLM)-based. A scenario-based chatbot generates a response within a predefined scenario for a user query on a specific domain or topic. The chatbot's response for recommendation is processed in conjunction with a separate algorithm. A LLM-based chatbot generates a response through a pretrained model to a user query on a wide range of topics. In this process, the LLM-based chatbot's response takes the form of a kind of recommendation, which is different from the existing recommendation services. To look at the issue more comprehensively, this paper examines recommendation-style system responses of a LLM-based chatbot with the principles of AI ethics. Several examples are shown where the chatbot's responses are modified according to principles of AI ethics.

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