Trusted Artificial Intelligence: Towards Certification of Machine Learning Applications

• URL: http://arxiv.org/abs/2103.16910v1
• Date: Wed, 31 Mar 2021 08:59:55 GMT
• Title: Trusted Artificial Intelligence: Towards Certification of Machine Learning Applications
• Authors: Philip Matthias Winter, Sebastian Eder, Johannes Weissenb\"ock, Christoph Schwald, Thomas Doms, Tom Vogt, Sepp Hochreiter, Bernhard Nessler
• Abstract summary: The T"UV AUSTRIA Group in cooperation with the Institute for Machine Learning at the Johannes Kepler University Linz proposes a certification process. The holistic approach attempts to evaluate and verify the aspects of secure software development, functional requirements, data quality, data protection, and ethics. The audit catalog can be applied to low-risk applications within the scope of supervised learning.
• Score: 5.7576910363986
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial Intelligence is one of the fastest growing technologies of the 21st century and accompanies us in our daily lives when interacting with technical applications. However, reliance on such technical systems is crucial for their widespread applicability and acceptance. The societal tools to express reliance are usually formalized by lawful regulations, i.e., standards, norms, accreditations, and certificates. Therefore, the T\"UV AUSTRIA Group in cooperation with the Institute for Machine Learning at the Johannes Kepler University Linz, proposes a certification process and an audit catalog for Machine Learning applications. We are convinced that our approach can serve as the foundation for the certification of applications that use Machine Learning and Deep Learning, the techniques that drive the current revolution in Artificial Intelligence. While certain high-risk areas, such as fully autonomous robots in workspaces shared with humans, are still some time away from certification, we aim to cover low-risk applications with our certification procedure. Our holistic approach attempts to analyze Machine Learning applications from multiple perspectives to evaluate and verify the aspects of secure software development, functional requirements, data quality, data protection, and ethics. Inspired by existing work, we introduce four criticality levels to map the criticality of a Machine Learning application regarding the impact of its decisions on people, environment, and organizations. Currently, the audit catalog can be applied to low-risk applications within the scope of supervised learning as commonly encountered in industry. Guided by field experience, scientific developments, and market demands, the audit catalog will be extended and modified accordingly.

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