Exploring Gender Biases in Language Patterns of Human-Conversational Agent Conversations

TL;DR

The paper tackles how gendered design in conversational agents can activate gender biases in human-AI interactions and potentially transfer biases to human-human communication. It proposes a mixed-methods research plan including dyadic observational studies with gendered CAs, an online experiment, and LIWC-based text analysis to examine gendered language, engagement, and credibility. Preliminary observations indicate females as CAs are more anthropomorphized but may be evaluated less favorably, with male CAs eliciting stronger rapport in certain user dyads; users also tend to import human communication norms into machine interactions. The study aims to inform ethical and equitable CA design by identifying when gender cues are beneficial or harmful and by developing theory-building in HCI that links interpersonal communication with human-AI interactions.

Abstract

With the rise of human-machine communication, machines are increasingly designed with humanlike characteristics, such as gender, which can inadvertently trigger cognitive biases. Many conversational agents (CAs), such as voice assistants and chatbots, default to female personas, leading to concerns about perpetuating gender stereotypes and inequality. Critiques have emerged regarding the potential objectification of females and reinforcement of gender stereotypes by these technologies. This research, situated in conversational AI design, aims to delve deeper into the impacts of gender biases in human-CA interactions. From a behavioral and communication research standpoint, this program focuses not only on perceptions but also the linguistic styles of users when interacting with CAs, as previous research has rarely explored. It aims to understand how pre-existing gender biases might be triggered by CAs' gender designs. It further investigates how CAs' gender designs may reinforce gender biases and extend them to human-human communication. The findings aim to inform ethical design of conversational agents, addressing whether gender assignment in CAs is appropriate and how to promote gender equality in design.