Discovering Systematic Errors in Machine Learning Models with Cross-Modal Embeddings
Published:Apr 7, 2022 07:00
•1 min read
•Stanford AI
Analysis
This article from Stanford AI introduces Domino, a novel approach for identifying systematic errors in machine learning models. It highlights the importance of understanding model performance on specific data slices, where a slice represents a subset of data sharing common characteristics. The article emphasizes that high overall accuracy can mask significant underperformance on particular slices, which is crucial to address, especially in safety-critical applications. Domino and its evaluation framework offer a valuable tool for practitioners to improve model robustness and make informed deployment decisions. The availability of a paper, walkthrough, GitHub repository, documentation, and Google Colab notebook enhances the accessibility and usability of the research.
Key Takeaways
- •Domino is a new approach for discovering systematic errors in ML models.
- •Understanding model performance on data slices is crucial for reliable deployment.
- •Slice awareness is particularly important in safety-critical applications.
Reference
“Machine learning models that achieve high overall accuracy often make systematic errors on coherent slices of validation data.”