Orlando Marquez Ayala


pdf bib
Azimuth: Systematic Error Analysis for Text Classification
Gabrielle Gauthier-melancon | Orlando Marquez Ayala | Lindsay Brin | Chris Tyler | Frederic Branchaud-charron | Joseph Marinier | Karine Grande | Di Le
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We present Azimuth, an open-source and easy-to-use tool to perform error analysis for text classification. Compared to other stages of the ML development cycle, such as model training and hyper-parameter tuning, the process and tooling for the error analysis stage are less mature. However, this stage is critical for the development of reliable and trustworthy AI systems. To make error analysis more systematic, we propose an approach comprising dataset analysis and model quality assessment, which Azimuth facilitates. We aim to help AI practitioners discover and address areas where the model does not generalize by leveraging and integrating a range of ML techniques, such as saliency maps, similarity, uncertainty, and behavioral analyses, all in one tool. Our code and documentation are available at github.com/servicenow/azimuth.