Designing Inclusive ML Models through Data Design Practices
Summary:
- Building inclusive machine learning (ML) models requires careful consideration of data design practices.
- Novice-oriented ML modeling tools often do not educate users on data diversity and data quality.
- Researchers have outlined four data design practices (DDPs) to guide the design of inclusive ML models.
- A tablet-based application called Co-ML has been developed to teach novice users about DDPs.
Author’s Take:
Designing inclusive ML models necessitates understanding and implementing data design practices. Novice ML users often lack the knowledge of how to create representative datasets. In order to address this gap, researchers have developed Co-ML, a tablet-based application, to educate users about data diversity and data quality through collaborative ML modeling. By incorporating DDPs into ML modeling tools, novices can learn to design inclusive models that truly reflect real-world data.