AI-Mediated Communication: Language Use and Interpersonal Effects in a Referential Communication Task
AI-Mediated Communication: Language Use and Interpersonal Effects in a Referential Communication Task
复制标题
人工智能介导的沟通：参考沟通任务中的语言使用和人际效应
DOI：
10.1145/3449091
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发表时间：
2021
影响因子：
--
通讯作者：
Hohenstein, Jess
中科院分区：
文献类型：
--
作者：
Mieczkowski, Hannah;Hancock, Jeffrey T.;Naaman, Mor;Jung, Malte;Hohenstein, Jess
人工智能介导的通信（AI-MC）是人际通信，涉及人工智能系统，可以修改，增强甚至生成内容，以实现沟通和关系目标。AI-MC越来越多地参与人类交流，并有可能影响人类交流的核心方面，如语言产生，人际感知和任务表现。通过受试者之间的实验设计，我们研究了当将人工智能生成的语言以建议的文本响应（谷歌的智能回复）的形式整合到基于文本的参考通信任务中时，这些过程是如何受到影响的。我们的研究复制和扩展了积极性偏见在人工智能生成的语言中的影响，并将邻接对框架引入到人工智能-MC的研究中。我们还发现了初步但混合的证据，表明人工智能生成的语言有可能破坏人际感知的某些方面，例如社会吸引力。这项研究为AI-MC未来的工作提供了重要的概念，并为人与人之间的交流中的AI系统设计提供了影响。
AI-Mediated Communication (AI-MC) is interpersonal communication that involves an artificially intelligent system that can modify, augment, or even generate content to achieve communicative and relational goals. AI-MC is increasingly involved in human communication and has the potential to impact core aspects of human communication, such as language production, interpersonal perception and task performance. Through a between-subjects experimental design we examine how these processes are influenced when integrating AI-generated language in the form of suggested text responses (Google's smart replies) into a text-based referential communication task. Our study replicates and extends the impacts of a positivity bias in AI-generated language and introduces the adjacency pair framework into the study of AI-MC. We also find preliminary yet mixed evidence to suggest that AI-generated language has the potential to undermine some dimensions of interpersonal perception, such as social attraction. This study contributes important concepts for future work in AI-MC and offers findings with implications for the design of AI systems in human-to-human communication.