With widespread adoption of AI models for important decision making, ensuring reliability of such models remains
an important challenge. In this paper, we present an end-toend generic framework for testing AI Models which performs
automated test generation for different modalities such as text,
tabular, and time-series data and across various properties such
as accuracy, fairness, and robustness. Our tool has been used for
testing industrial AI models and was very effective to uncover
issues present in those models.
Demo video link- https://youtu.be/984UCU17YZI
Arxiv page: https://arxiv.org/abs/2102.06166 Pdf: https://arxiv.org/pdf/2102.06166.pdf
Abstract
With widespread adoption of AI models for important decision making, ensuring reliability of such models remains an important challenge. In this paper, we present an end-toend generic framework for testing AI Models which performs automated test generation for different modalities such as text, tabular, and time-series data and across various properties such as accuracy, fairness, and robustness. Our tool has been used for testing industrial AI models and was very effective to uncover issues present in those models. Demo video link- https://youtu.be/984UCU17YZI