Bias-Aware Generative XAI for Sustainable Air Traffic Control: A Methodological Framework with Predictive Telemetry

Artificial Intelligence (AI) is reshaping decision-making across industries, but in life-critical domains like Air Traffic Control (ATC), performance alone is not enough—AI must also be transparent, accountable, and human-centered. At the same time, ecological sustainability (e.g., fuel efficiency, emissions reduction, noise abatement) is becoming a key operational priority. Yet under high workload or time pressure, these goals are often deprioritized. This paper introduces a novel framework for bias-aware human–AI teaming in ATC, integrating Generative Explainable AI (genXAI), Predictive Telemetry (PT), multimodal machine learning, and mechanistic interpretability. Designed for Air Traffic Controllers (ATCOs), the system anticipates future traffic states, detects bias-prone conditions, and delivers adaptive, context-sensitive explanations. Through bias-aware interfaces, serious-game training, and neuroadaptive feedback loops, the framework aligns decision support with cognitive state and operational complexity. The framework targets three key objectives: (1) mitigating cognitive biases, (2) improving ground routing decisions, and (3) validating anticipatory AI models. By reframing sustainability as a human–AI collaboration challenge, this work advances a new class of trustworthy, bias-aware AI that enhances both safety and ecological performance in Air Traffic Management (ATM).