Testing Framework for Black-box AI Models

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, tab...

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Veröffentlicht in:2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) S. 81 - 84
Hauptverfasser: Aggarwal, Aniya, Shaikh, Samiulla, Hans, Sandeep, Haldar, Swastik, Ananthanarayanan, Rema, Saha, Diptikalyan
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2021
Schlagworte:
Artificial intelligence
Decision making
Robustness
Software engineering
Test pattern generators
Testing
ISBN:1665412194, 9781665412193
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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
ISBN:1665412194
9781665412193
DOI:10.1109/ICSE-Companion52605.2021.00041