Descripción del proyecto
Inteligencia artificial y transparencia en la gestión del tráfico aéreo
Se espera que tecnologías como la inteligencia artificial (IA) y el aprendizaje automático (AA) ofrezcan importantes mejoras a la gestión del tráfico aéreo (GTA), y den lugar una automatización completa que aumente la capacidad. No obstante, la fiabilidad y la seguridad de estos sistemas sigue siendo fundamental para usuarios y operadores y es un escollo fundamental que se interpone a la adopción de estas dos tecnologías en cualquier ámbito. El objetivo principal del proyecto financiado con fondos europeos TAPAS es ofrecer un conjunto de principios y criterios que faciliten la introducción de estas tecnologías en el sector de la GTA de un modo seguro y confiable. Mediante técnicas como «eXplainable Artificial Intelligence» (XAI) y análisis visual se ayudará a explorar las contrapartidas entre la eficacia del uso de la IA y la idoneidad de su instalación en aplicaciones concretas.
Objetivo
As Artificial Intelligence (AI) becomes an increasing part of our lives in general, individuals are finding that the need to trust these AI based systems is paramount. Air Traffic Management (ATM) is not an stranger to this: with a system close to, or already at, a saturation level, AI applications are considered a main enabler to reach higher levels of automation.
This would mean a fundamental shift in the automation approach when moving from the classical human-machine interaction to a potentially much richer solution enabled by these AI systems, in which trust in the operations needs to be generated. As humans, operators must be able to fully understand how decisions are being made so that they can trust the decisions of AI systems. The lack of explainability and trust hampers the ability (both individual and global) to fully trust AI systems.
TAPAS aims at exploring highly automated AI-based scenarios through analysis and experimental activities applying eXplainable Artificial Intelligence (XAI) and Visual Analytics, in order to derive general principles of transparency which pave the way for the application of these AI technologies in ATM environments, enabling higher levels of automation.
Specifically, TAPAS will:
• Analyse two operational environments: ATC (Air Traffir Control)Conflict Detection & Resolution (tactical), and Air Traffic Flow Management (pre-tactical). For them, levels of automation 1 to 3 according to SESAR Model will be considered.
• Develop eXplainable Artificial Intelligence (XAI) prototypes addressing the requirements and acceptability criteria of the scenarios.
• Run experiments that assess the applicability of these XAI modules in the higher levels of automation considered, exploring different ways of interaction and information exchange.
• Apply Visual Analytics techniques to contribute to explainability of decissions.
• Extract conclusions, principles and recommendations related to transparency of AI in ATM.
Ámbito científico
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Régimen de financiación
RIA - Research and Innovation actionCoordinador
28022 Madrid
España