Identifying and managing risks of AI-driven operations: A case study of automatic speech recognition for improving air traffic safety

Abstract

In this work, the primary focus is to identify potential technical risks of Artificial Intelligence (AI)-driven operations for the safety monitoring of the air traffic from the perspective of speech communication by studying the representative case and evaluating user experience. The case study is performed to evaluate the AI-driven techniques and applications using objective metrics, in which several risks and technical facts are obtained to direct future research. Considering the safety–critical specificities of the air traffic control system, a comprehensive subjective evaluation is conducted to collect user experience by a well-designed anonymous questionnaire and a face-to-face interview. In this procedure, the potential risks obtained from the case study are confirmed, and the impacts on human working are considered. Both the case study and the evaluation of user experience provide compatible results and conclusions: (A) the proposed solution is promising to improve the traffic safety and reduce the workload by detecting potential risks in advance; (B) the AI-driven techniques and whole diagram are suggested to be enhanced to eliminate the possible distraction to the attention of air traffic controllers. Finally, a variety of strategies and approaches are discussed to explore their capability to advance the proposed solution to industrial practices.