  Conversational recommender systems have demonstrated great success. They can
accurately capture a user's current detailed preference -- through a
multi-round interaction cycle -- to effectively guide users to a more
personalized recommendation. Alas, conversational recommender systems can be
plagued by the adverse effects of bias, much like traditional recommenders. In
this work, we argue for increased attention on the presence of and methods for
counteracting bias in these emerging systems. As a starting point, we propose
three fundamental questions that should be deeply examined to enable fairness
in conversational recommender systems.
