Automated Testing of AI Models
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 33 references that are in the map. First posted on arXiv 7 Oct 2021.
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References · 14
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
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- 2016Adversarial examples in the physical worldcited by 1,769
- 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 1,006
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- 2018WaveGlow: A Flow-based Generative Network for Speech Synthesiscited by 309
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- 2019Machine Learning Testing: Survey, Landscapes and Horizonscited by 218
- 2017Beyond Sparsity: Tree Regularization of Deep Models for Interpretabilitycited by 68
- 2018Concolic Testing for Deep Neural Networkscited by 51
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