# Examining the Values Reflected by Children during AI Problem Formulation

arXiv: https://arxiv.org/abs/2309.15839 · HTML: https://mlsanity.com/paper/2309.15839/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 27 Sep 2023.

## Builds on (most cited first, 4 of 4)

- [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020)
- [Problem Formulation and Fairness](https://mlsanity.com/paper/1901.02547/) (2019)
- [Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamics](https://mlsanity.com/paper/2005.07572/) (2020)
- [Exploring Machine Teaching with Children](https://mlsanity.com/paper/2109.11434/) (2021)

## Abstract (arXiv)

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.