# Collaborative Machine Learning Model Building with Families Using Co-ML

arXiv: https://arxiv.org/abs/2304.05444 · HTML: https://mlsanity.com/paper/2304.05444/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 48 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Builds on (most cited first, 3 of 3)

- [Crowdsourcing the Perception of Machine Teaching](https://mlsanity.com/paper/2002.01618/) (2020)
- [Designing AI Learning Experiences for K-12: Emerging Works, Future Opportunities and a Design Framework](https://mlsanity.com/paper/2009.10228/) (2020)
- [Exploring Machine Teaching with Children](https://mlsanity.com/paper/2109.11434/) (2021)

## Built on by (most cited first, 2 of 2)

- [Botender: Supporting Communities in Collaboratively Designing AI Agents through Case-Based Provocations](https://mlsanity.com/paper/2509.25492/) (2025)
- [InFL-UX: A Toolkit for Web-Based Interactive Federated Learning](https://mlsanity.com/paper/2503.04318/) (2025)

## Abstract (arXiv)

Existing novice-friendly machine learning (ML) modeling tools center around a solo user experience, where a single user collects only their own data to build a model. However, solo modeling experiences limit valuable opportunities for encountering alternative ideas and approaches that can arise when learners work together; consequently, it often precludes encountering critical issues in ML around data representation and diversity that can surface when different perspectives are manifested in a group-constructed data set. To address this issue, we created Co-ML -- a tablet-based app for learners to collaboratively build ML image classifiers through an end-to-end, iterative model-building process. In this paper, we illustrate the feasibility and potential richness of collaborative modeling by presenting an in-depth case study of a family (two children 11 and 14-years-old working with their parents) using Co-ML in a facilitated introductory ML activity at home. We share the Co-ML system design and contribute a discussion of how using Co-ML in a collaborative activity enabled beginners to collectively engage with dataset design considerations underrepresented in prior work such as data diversity, class imbalance, and data quality. We discuss how a distributed collaborative process, in which individuals can take on different model-building responsibilities, provides a rich context for children and adults to learn ML dataset design.