Towards Transparent AI Agents for Air Traffic Control

Abstract

Advances in the use of artificial intelligence agents for air traffic control (ATC) have the potential to reshape modern aviation operations. Despite these advances, the adoption of such agents in safety-critical ATC applications remains limited and this is, in part, due to their lack of transparency. The transparency of such agents is crucial for humans to understand why a specific action is advised or to reason about the agent's behaviour and assess their trustworthiness in real-time. The simpler the agent, the more transparent it can be, but this comes at the cost of flexibility in agent performance. We propose, illustrate, and critically examine mechanisms for providing AI agents for ATC with interpretability and explainability. We focus on agents ranging from simple rule-based systems to optimisation-based and reinforcement-learning agents, and we report initial qualitative feedback from operational ATCOs on prototype explainability mechanisms.