  As language models become increasingly integrated into our digital lives,
Personalized Text Generation (PTG) has emerged as a pivotal component with a
wide range of applications. However, the bias inherent in user written text,
often used for PTG model training, can inadvertently associate different levels
of linguistic quality with users' protected attributes. The model can inherit
the bias and perpetuate inequality in generating text w.r.t. users' protected
attributes, leading to unfair treatment when serving users. In this work, we
investigate fairness of PTG in the context of personalized explanation
generation for recommendations. We first discuss the biases in generated
explanations and their fairness implications. To promote fairness, we introduce
a general framework to achieve measure-specific counterfactual fairness in
explanation generation. Extensive experiments and human evaluations demonstrate
the effectiveness of our method.
