mlsanityData to 7 Oct 2026

Testing Framework for Black-box AI Models

arXiv 2102.06166 ↗First posted 11 Feb 2021cs.LG cs.AIcited by 3

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Abstract (arXiv)

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-to-end 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

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 4 of its 15 references that are in the map. First posted on arXiv 11 Feb 2021.

Builds on

4 of 15 references.

References · 4

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 979
  3. 2019Machine Learning Testing: Survey, Landscapes and Horizonscited by 213
  4. 2019Metamorphic Testing of a Deep Learning based Forecastercited by 3

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Cited by year

Citing papers per year: 2021: 1; 2022: 2.

3 papers.

  1. 2022pvCNN: Privacy-Preserving and Verifiable Convolutional Neural Network Testingcited by 20
  2. 2022Quantitative AI Risk Assessments: Opportunities and Challengescited by 4
  3. 2021Automated Testing of AI Modelscited by 0