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The growing popularity of language models has sparked interest inconversational recommender systems (CRS) within both industry and researchcircles. However, concerns regarding biases in these systems have emerged.While individual components of CRS have been subject to bias studies, aliterature gap remains in understanding specific biases unique to CRS and howthese biases may be amplified or reduced when integrated into complex CRSmodels. In this paper, we provide a concise review of biases in CRS bysurveying recent literature. We examine the presence of biases throughout thesystem's pipeline and consider the challenges that arise from combiningmultiple models. Our study investigates biases in classic recommender systemsand their relevance to CRS. Moreover, we address specific biases in CRS,considering variations with and without natural language understandingcapabilities, along with biases related to dialogue systems and languagemodels. Through our findings, we highlight the necessity of adopting a holisticperspective when dealing with biases in complex CRS models.

Tidying Up the Conversational Recommender Systems' Biases