Automated Testing of AI Models

TL;DR

This paper extends the AITEST framework to include testing techniques for image and speech-to-text models, as well as interpretability testing for tabular models, enhancing AI model reliability assessment.

Contribution

The paper introduces new testing capabilities for image and speech-to-text models and adds interpretability testing for tabular models within the AITEST framework.

Findings

Extended AITEST to cover image and speech-to-text models

Added interpretability testing for tabular models

Demonstrated comprehensive testing of diverse AI models

Abstract

The last decade has seen tremendous progress in AI technology and applications. With such widespread adoption, ensuring the reliability of the AI models is crucial. In past, we took the first step of creating a testing framework called AITEST for metamorphic properties such as fairness, robustness properties for tabular, time-series, and text classification models. In this paper, we extend the capability of the AITEST tool to include the testing techniques for Image and Speech-to-text models along with interpretability testing for tabular models. These novel extensions make AITEST a comprehensive framework for testing AI models.