Rethinking Certification for Trustworthy Machine Learning-Based Applications

• URL: http://arxiv.org/abs/2305.16822v4
• Date: Sun, 22 Oct 2023 19:31:17 GMT
• Title: Rethinking Certification for Trustworthy Machine Learning-Based Applications
• Authors: Marco Anisetti and Claudio A. Ardagna and Nicola Bena and Ernesto Damiani
• Abstract summary: Machine Learning (ML) is increasingly used to implement advanced applications with non-deterministic behavior. Existing certification schemes are not immediately applicable to non-deterministic 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.
• Score: 3.886429361348165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine Learning (ML) is increasingly used to implement advanced applications with non-deterministic behavior, which operate on the cloud-edge continuum. The pervasive adoption of ML is urgently calling for assurance solutions assessing applications non-functional properties (e.g., fairness, robustness, privacy) with the aim to improve their trustworthiness. Certification has been clearly identified by policymakers, regulators, and industrial stakeholders as the preferred assurance technique to address this pressing need. Unfortunately, existing certification schemes are not immediately applicable to non-deterministic 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.

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