Year: 2021
Type: preprint
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.
Source: arXiv (Cornell University)
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3204919792