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Gender affordances of conversational agents

机译:对话代理人的性别能力

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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.
机译:对话主体被赋予类人特征。特别是,通常认为他们有性别。有证据表明,性别建立了期望,这些期望会影响用户与代理商的体验。本文的目的是探讨对话代理人的性别承受能力。我们的考试采用整体方法来分析对9个聊天机器人的性别刻板印象的应用:六个具体化(三个男性和三个女性),两个具体化(男性和女性)以及一个机器人体现。在社会心理学研究的基础上,我们测试了性别定型观念在对话话题的选择以及抑制和言语虐待方面的持久性。我们的研究基于交互日志的定量文本分析。为此研究开发了英语性语和贬义词词典。结果表明,性别刻板印象倾向于在关系(风格)级别而不是在参照(内容)级别上影响交互。人们将负面刻板印象归因于呈现女性的聊天机器人,而不是归因于呈现男性的聊天机器人,而呈现女性的聊天机器人更多地是隐性和显性的性爱对象和咒骂对象。最后,我们呼吁对用户交互进行更全面的分析,并考虑所有范围的用户交互。

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