THE USE OF ARTIFICIAL INTELLIGENCE IN THE PROCESS OF CERTIFICATION OF AVIATION EQUIPMENT

Abstract

The article examines the role and prospects of applying artificial intelligence (AI) in the certification process of aviation technology. It is emphasized that traditional certification system rely on deterministic verification methods and require full predictability of system, whereas machine learning algorithms operate on a probabilistic basis, creating significant challenges for verification and validation.

The study highlights key areas of AI integration, including generative design, digital twins, automated quality control, predictive maintenance, and autonomous manufacturing systems. These technologies accelerate aircraft development cycles, reduce costs, and improve safety levels, yet simultaneously demand a rethinking of regulatory frameworks.

The paper analyzes international practices, particularly the initiatives of the European Union Aviation Safety Agency (EASA), which has introduced the concept of “Human-AI Teaming” and the special condition SC-AI-01, and the Federal Aviation Administration (FAA), which follows a cautious, step-by-step approach with an emphasis on preserving human accountability. New approaches to certification are outlined, such as data-driven digital management, unified platforms for compliance assessment, and hybrid verification methods that combine simulation testing with formal analysis.

The findings suggest that AI integration in aviation is an irreversible trend that transforms the certification paradigm itself: from evaluating a static product to assessing AI-driven processes. This requires the development of new international standards, harmonization of regulatory practices, and creation of methodologies ensuring the reliability and safety of AI systems throughout the entire life cycle of aviation technology.