Bailey Jakki O, Patel Barkha, Gurari Danna
School of Information, University of Texas at Austin, Austin, TX, United States.
Front Artif Intell. 2021 May 13;4:637532. doi: 10.3389/frai.2021.637532. eCollection 2021.
Artificial intelligence (AI)-powered technologies are becoming an integral part of youth's environments, impacting how they socialize and learn. Children (12 years of age and younger) often interact with AI through conversational agents (e.g., Siri and Alexa) that they speak with to receive information about the world. Conversational agents can mimic human social interactions, and it is important to develop socially intelligent agents appropriate for younger populations. Yet it is often unclear what data are curated to power many of these systems. This article applies a sociocultural developmental approach to examine child-centric intelligent conversational agents, including an overview of how children's development influences their social learning in the world and how that relates to AI. Examples are presented that reflect potential data types available for training AI models to generate children's conversational agents' speech. The ethical implications for building different datasets and training models using them are discussed as well as future directions for the use of social AI-driven technology for children.
人工智能驱动的技术正成为青少年生活环境中不可或缺的一部分，影响着他们社交和学习的方式。儿童（12岁及以下）经常通过对话代理（如Siri和Alexa）与人工智能互动，通过与它们交谈来获取有关世界的信息。对话代理可以模仿人类的社交互动，开发适合年轻人群体的具有社交智能的代理非常重要。然而，通常不清楚为这些系统中的许多系统提供动力的数据是如何策划的。本文采用社会文化发展方法来研究以儿童为中心的智能对话代理，包括概述儿童的发展如何影响他们在世界中的社会学习以及这与人工智能的关系。文中给出了一些例子，这些例子反映了可用于训练人工智能模型以生成儿童对话代理语音的潜在数据类型。还讨论了构建不同数据集并使用它们训练模型的伦理意义以及将社会人工智能驱动技术用于儿童的未来方向。
