Overall, the goal of TRUSTY is to provide adaptation in the level of transparency to enhance the trustworthiness of AI-powered decisions in the context of RDTs. While in an actual tower, operators have direct visual access to the taxiway and runway monitoring, the RDTs concept only provides such information through video transmission with
a warning and the corresponding explanation. To deliver trustworthiness in an AI-powered intelligent system TRUSTY will consider several approaches, and they are listed:
• ‘Self-explainable and Self-learning’ system for critical decision-making
• ‘Transparent ML’ models incorporating interpretability, fairness, and accountability
• ‘Interactive data visualization and multimodal human-machine interface/interactions (HMI), i.e. Graphical User Interface (GUI)’ for smart and efficient decision support
• ‘Adaptive level of explanation’ regarding the user's cognitive state.
• “HCAI” to enhance the trustworthiness of AI-powered systems.
• “Human-machine collaboration (HMC) or Human-AI teaming (HAIT)” to consider user feedback to insure some computation flexibility and the users’ acceptability.
The measurable objectives are categories: i) Scientific, innovation, and research objectives (SIO) focusing on the research to deliver a rigorous and self-standing methodology to drive the implementation and define its operational principles; ii) Technological objectives (TO) focusing on a prototypical system and the delivery
and deployment in close to the real environment; iii) User-centric design (UCD) focusing on user acceptability in the domain of RDTs and iv) Impact and societal objectives (IO) with a specific focus on providing relevant impact and considering socio-economic aspects, evaluation of (cost) effectiveness and the scalability potential of the ecosystem,
the spread of excellence gained and applicability for the ATM ecosystem.
