Revealing Persona Biases in Dialogue Systems

TL;DR

This study investigates how adopting demographic personas in dialogue systems influences biases and harmful responses, revealing that persona choices can both mitigate and exacerbate biases, emphasizing the need for systematic evaluation.

Contribution

The paper presents the first large-scale analysis of persona biases in dialogue systems and introduces an open-source framework for their exploration and assessment.

Findings

Adopting personas can decrease harmful responses compared to no persona.

Persona choices influence the level of bias and harm in generated responses.

Different personas can cause varying degrees of harm towards specific demographic groups.

Abstract

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.