Conversational agents are attributed humanlike characteristics; in particular, they are often assumed to have a gender. There is evidence that gender sets up expectations that have an impact on user experiences with agents. The objective of this paper is to explore gender affordances of conversational agents. Our examination takes a holistic approach to the analysis of the application of gender stereotypes to nine chatterbots: six embodied (three male and three female), two disembodied (male and female), and a robot embodiment. Building on social psychology research, we test the persistence of gender stereotypes in the selection of conversation topics and in the elicitation of disinhibition and verbal abuse. Our study is based on quantitative textual analysis of interaction logs. A dictionary of English sexual slang and derogatory terms was developed for this study. Results show that gender stereotypes tend to affect interaction more at the relational (style) level then at the referential (content) level of conversation. People attribute negative stereotypes to female-presenting chatterbots more often than they do to male-presenting chatterbots, and female-presenting chatterbots are more often the objects of implicit and explicit sexual attention and swear words. We conclude by calling for a more informed analysis of user interactions that considers the full range of user interactions.
Stored only when both papers are in the corpus — 148 citations exist upstream.