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With the rise of human-machine communication, machines are increasinglydesigned with humanlike characteristics, such as gender, which caninadvertently trigger cognitive biases. Many conversational agents (CAs), suchas voice assistants and chatbots, default to female personas, leading toconcerns about perpetuating gender stereotypes and inequality. Critiques haveemerged regarding the potential objectification of females and reinforcement ofgender stereotypes by these technologies. This research, situated inconversational AI design, aims to delve deeper into the impacts of genderbiases in human-CA interactions. From a behavioral and communication researchstandpoint, this program focuses not only on perceptions but also thelinguistic styles of users when interacting with CAs, as previous research hasrarely explored. It aims to understand how pre-existing gender biases might betriggered by CAs' gender designs. It further investigates how CAs' genderdesigns may reinforce gender biases and extend them to human-humancommunication. The findings aim to inform ethical design of conversationalagents, addressing whether gender assignment in CAs is appropriate and how topromote gender equality in design.

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