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Rethinking Certification for Trustworthy Machine-Learning-Based Applications
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DOI：10.1109/MIC.2023.3322327.png)
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Machine learning (ML) is increasingly used to implement advanced applications with nondeterministic behavior, which operate on the cloud-edge continuum. The pervasive adoption of ML is urgently calling for assurance solutions to assess applications' nonfunctional properties (e.g., fairness, robustness, and privacy) with the aim of improving their trustworthiness. Certification has been clearly identified by policy makers, regulators, and industrial stakeholders as the preferred assurance technique to address this pressing need. Unfortunately, existing certification schemes are not immediately applicable to nondeterministic applications built on ML models. This article analyzes the challenges and deficiencies of current certification schemes, discusses open research issues, and proposes a first certification scheme for ML-based applications.
Keyword:
Certification
Robustness
Data models
Behavioral sciences
Malware
Security
Detectors
期刊
IF:
4.4
论文数:
2.0K
被引数:
2.0K
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