Female chatbots are helpful, male chatbots are competent?

Abstract

Chatbots have in recent years increasingly been used by organizations to interact with their customers. Interestingly, most of these chatbots are gendered as female, displaying stereotypical notions in their avatars, profile pictures and language. Considering the harmful effects associated with gender-based stereotyping at a societal level—and in particular the detrimental effects to women—it is crucial to understand the effects of such stereotyping when transferred and perpetuated by chatbots. The current study draws on the Stereotype Content Model (SCM) and explores how the warmth (high vs. low) of a chatbot’s language and the chatbot’s assigned gender elicit stereotypes that affect the perceived trust, helpfulness, and competence of the chatbot. In doing so, this study shows how established stereotype theory can be used as a framework for human-machine communication research. Moreover, its results can serve as a foundation to explore ways of mitigating the perpetuation of stereotyping and bring forward a broader discussion on ethical considerations for human-machine communication.

A Synthetic Voice for an Assistive Conversational Agent: A Survey to Discover Italian Preferences regarding Synthetic Voice’s Gender and Quality Level

“…Regarding the entrusted tasks to the synthetic voices, outcomes revealed that the HQ voice gendered as female was considered as better suited than the HQ male voice and both the LQ voices (female and male, respectively), in achieving front office and healthcare tasks. With regard to housework, a typical gender stereotype seems evident, according to which female-gendered CAs are considered more suitable for nurturing and caring occupations, while CAs gendered as male are preferred for those tasks requiring authority, competence, and agency [37,38]. Supporting this speculation is the fact that both HQ and LQ female voices were found to be better suited to perform household tasks than HQ and LQ male voices.…”

The impact of ChatGPT on higher education

“…The issue of stereotyping has mixed academic evidence. For example, Bastiansen et al (2022) deployed the Stereotype Content Model to research the effects of warmth and gender of a chatbot on stereotypes, trustworthiness, aid, and capability. They find no divergent outcomes stemming from exposure to heat and assigned gender.…”

Gender Bender Bot - The Effect of (Not) Following Gender Stereotypes in Conversational Agent Design

“…In this regard, an impactful effect is users' perception of gender in a CA (Gong, 2008), which is caused by the combination of several cues (i.e., name, avatar, and description) (Bastiansen et al, 2022;Feine et al, 2020). Thereby, gender can be defined as a non-essential category which is repeatedly performed based on societal norms (Morgenroth & Ryan, 2018).…”