Applications of natural language processing in aviation safety: A review and qualitative analysis

NLP can identify safety issues from unstructured data: The paper demonstrates that Natural Language Processing can help aviation safety by automatically analyzing written reports and communications to uncover critical safety patterns that might otherwise be missed, making the industry proactive rather than reactive in addressing hazards.

Data and interpretability remain major implementation barriers: The research identifies two significant challenges preventing wider adoption of NLP in aviation—the need for large amounts of labeled training data and difficulty understanding how complex AI models reach their safety conclusions, both critical concerns in a safety-critical industry.

Explainable AI is essential for aviation applications: The paper proposes that solutions like "explainable AI" should be prioritized, since aviation safety professionals need to understand why an algorithm flagged a potential risk before acting on that recommendation, rather than treating AI as a "black box."

Active learning can reduce data bottlenecks: The study suggests that "active learning" techniques could help address the data scarcity problem by making the annotation process more efficient, potentially accelerating NLP implementation in aviation without requiring massive pre-existing datasets.

The field is still emerging with significant growth potential: With only 34 published studies found in the Scopus database as of May 2024, the paper indicates NLP applications in aviation safety remain relatively underdeveloped, suggesting substantial opportunities for responsible development and deployment in this high-stakes industry.