A Perspective on Building Ethical Datasets for Children's Conversational Agents - Bailey, Jakki O.; Patel, Barkha; Gurari, Danna (Frontiers in Artificial Intelligence, 2021)
Artificial intelligence (AI)-powered technologies are becoming an integral part of children’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, Alexa) that they speak with to receive information about the world. Conversational agents can mimic human social interactions, and it is important to develop social intelligent agents appropriate for younger populations. Yet, it is often unclear what data is 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.