Dialogue systems in the form of chatbots and personal assistants are being
increasingly integrated into people's lives. Modern dialogue systems may
consider adopting anthropomorphic personas, mimicking societal demographic
groups to appear more approachable and trustworthy to users. However, the
adoption of a persona can result in the adoption of biases. In this paper, we
present the first large-scale study on persona biases in dialogue systems and
conduct analyses on personas of different social classes, sexual orientations,
races, and genders. We define persona biases as harmful differences in
responses (e.g., varying levels of offensiveness, agreement with harmful
statements) generated from adopting different demographic personas.
Furthermore, we introduce an open-source framework, UnitPersonaBias, to explore
and aggregate persona biases in dialogue systems. By analyzing the Blender and
DialoGPT dialogue systems, we observe that adopting personas can actually
decrease harmful responses, compared to not using any personas. Additionally,
we find that persona choices can affect the degree of harms in generated
responses and thus should be systematically evaluated before deployment. We
also analyze how personas can result in different amounts of harm towards
specific demographics.

本文研究了对话系统的人格偏见，并分析了不同社会阶级、性取向、种族和性别的人物角色。研究者提出将对话系统的角色升级到拥有更多人文特征以更好的迎合用户的趋势可能会产生一些偏见。他们还介绍了一个开源框架 UnitPersonaBias，以探索和聚合对话系统中的人格偏见。此外，研究者还发现与不使用人格形象相比，采用人格形象可能会减少有害的回应。但是，人格选择会影响生成响应中危害程度，因此在实际应用前应该进行系统评估。