The Natural Language Processing (NLP) services using machine learning have rich applications in solving various tasks, and have been widely deployed and used, usually accessible by API calls. The robustness of the NLP services is challenged by various well-known general corruptions and adversarial attacks. Examples of general corruptions include inadvertent or random deletion, addition, or repetition of characters or words. Adversarial attacks generate adversarial characters, words or sentence samples causing the models underpinning the NLP services to produce incorrect results. This standard proposes a method for quantitatively evaluating the robustness the NLP services. Under the method, different cases the evaluation needs to perform against are specified. Robustness metrics and their calculation are defined. With the standard, the service stakeholders including the service developer, service providers, and service users can develop understanding of the robustness of the services. The evaluation can be performed during various phases in the life cycle of the NLP services, the testing phase, in the validation phase, after deployment, etc.
- Sponsor Committee
- C/AISC - Artificial Intelligence Standards Committee
- Status
- Active Standard
- PAR Approval
- 2022-05-13
- Board Approval
- 2024-03-21
Working Group Details
- Society
- IEEE Computer Society
Learn More About IEEE Computer Society - Sponsor Committee
- C/AISC - Artificial Intelligence Standards Committee
- Working Group
-
RAIBS - Robustness of Artificial Intelligence Based Service
- IEEE Program Manager
- Christy Bahn
Contact Christy Bahn - Working Group Chair
- Qing An
Other Activities From This Working Group
Current projects that have been authorized by the IEEE SA Standards Board to develop a standard.
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Standards approved by the IEEE SA Standards Board that are within the 10-year lifecycle.
3129-2023
IEEE Standard for Robustness Testing and Evaluation of Artificial Intelligence (AI)-based Image Recognition Service
Test specifications with a set of indicators for common corruption and adversarial attacks, which can be used to evaluate the robustness of artificial intelligence-based image recognition services are provided in this standard. Robustness attack threats and establishes an assessment framework to evaluate the robustness of artificial intelligence-based image recognition service under various settings are also specified in this standard.
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