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We present Azimuth, an open-source and easy-to-use tool to perform erroranalysis for text classification. Compared to other stages of the MLdevelopment cycle, such as model training and hyper-parameter tuning, theprocess and tooling for the error analysis stage are less mature. However, thisstage is critical for the development of reliable and trustworthy AI systems.To make error analysis more systematic, we propose an approach comprisingdataset analysis and model quality assessment, which Azimuth facilitates. Weaim to help AI practitioners discover and address areas where the model doesnot generalize by leveraging and integrating a range of ML techniques, such assaliency maps, similarity, uncertainty, and behavioral analyses, all in onetool. Our code and documentation are available atgithub.com/servicenow/azimuth.

Azimuth: Systematic Error Analysis for Text Classification