Examining the Values Reflected by Children during AI Problem Formulation

TL;DR

This study explores how children conceptualize and embed their values into AI systems during problem formulation, revealing their emphasis on social understanding and advanced system intelligence.

Contribution

It introduces a co-design method with children to analyze their values in AI design, using a psychological framework to interpret their ideas.

Findings

Children value social context and family in AI systems.

Children envision AI with emotion detection and social understanding.

AI models should handle multiple modalities and correct errors through data.

Abstract

Understanding how children design and what they value in AI interfaces that allow them to explicitly train their models such as teachable machines, could help increase such activities' impact and guide the design of future technologies. In a co-design session using a modified storyboard, a team of 5 children (aged 7-13 years) and adult co-designers, engaged in AI problem formulation activities where they imagine their own teachable machines. Our findings, leveraging an established psychological value framework (the Rokeach Value Survey), illuminate how children conceptualize and embed their values in AI systems that they themselves devise to support their everyday activities. Specifically, we find that children's proposed ideas require advanced system intelligence, e.g. emotion detection and understanding the social relationships of a user. The underlying models could be trained under multiple modalities and any errors would be fixed by adding more data or by anticipating negative examples. Children's ideas showed they cared about family and expected machines to understand their social context before making decisions.