VerifAI: Framework for Functional Verification of AI based Systems in the Maritime Domain

With the continuous emergence and steady development of new technologies the way for Maritime Autonomous Surface Ship (MASS) is being paved. However, this manifold of available and imminent technologies challenges regulatory bodies and auditing authorities. Technologies which make use of Artificial Intelligence (AI), in particular Machine Learning (ML), play a special role. On one hand, they are not covered by current regulations or audit processes and, on the other hand, they may represent black boxes whose behaviours are not readily explainable and thus impede audit processes even further. In an upcoming study titled VerifAI the authors focus on this gap within European and German regulatory bodies and auditing authorities. The technological scope lies on MASS-related products which rely on partially or fully AI based systems. In the present article the original authors summarize the outlined study. The authors review the current regulatory status concerning audit processes and the market situation concerning available and imminent (partially) AI based systems of MASS-related products. To close the gap a conceptual, integrated framework consisting of a Safety Guideline for the manufacturers and a Verification Guideline for the auditing authorities is presented. The framework aims to give regulatory bodies and auditing authorities an overview of necessary steps for robust verification of safe products without hindering innovation or requiring in-depth knowledge about the (black box-like) systems. The results are condensed into recommendations for actions, listing the most important results, and proposing entry points as well as future research in the field of verifying (partially) AI-based MASS-related products.

• Record URL:
  • http://dx.doi.org/10.12716/1001.18.03.12
• Record URL:
  • https://www.transnav.eu/Article_VerifAI_Framework_for_Functional_Stach,71,1432.html
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/20836473
• Supplemental Notes:
  • © 2024 The Authors.
• Authors:
  • Stach, T
  • Koch, P
  • Constapel, M
  • Portier, M
  • Schmid, H
• Publication Date: 2024

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 585-591
• Serial:
  • TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation
  • Volume: 18
  • Issue Number: 3
  • Publisher: Akademia Morska w Gdyni
  • ISSN: 2083-6473
  • EISSN: 2083-6481
  • Serial URL: http://www.transnav.eu/
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Artificial intelligence; Autonomous vehicles; Machine learning; Maritime safety; Regulations; Ships
• Identifier Terms: Safety of Life at Sea Convention
• Geographic Terms: Europe; Germany
• Subject Areas: Data and Information Technology; Law; Marine Transportation; Safety and Human Factors;

Filing Info

• Accession Number: 01935529
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 30 2024 11:16AM