Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children

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Abstract

AI systems are becoming increasingly pervasive within children's devices, apps, and services. However, it is not yet well-understood how risks and ethical considerations of AI relate to children. This paper makes three contributions to this area: first, it identifies ten areas of alignment between general AI frameworks and codes for age-appropriate design for children. Then, to understand how such principles relate to real application contexts, we conducted a landscape analysis of children's AI systems, via a systematic literature review including 188 papers. This analysis revealed a wide assortment of applications, and that most systems' designs addressed only a small subset of principles among those we identified. Finally, we synthesised our findings in a framework to inform a new "Code for Age-Appropriate AI", which aims to provide timely input to emerging policies and standards, and inspire increased interactions between the AI and child-computer interaction communities.

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Wang, G., Zhao, J., Van Kleek, M., & Shadbolt, N. (2022). Informing Age-Appropriate AI: Examining Principles and Practices of AI for Children. In Conference on Human Factors in Computing Systems - Proceedings. Association for Computing Machinery. https://doi.org/10.1145/3491102.3502057

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