Examining the Values Reflected by Children during AI Problem Formulation
This research addresses the problem of aligning AI design with children's values to guide future technology development, though it is incremental as it builds on existing frameworks and small-scale co-design sessions.
The study investigated how children embed their values into AI systems they design, finding that their ideas require advanced intelligence like emotion detection and understanding social relationships, with errors addressed by adding more data or anticipating negative examples.
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.