TRUSTY
Financiado
TRUSTWORTHY INTELLIGENT SYSTEM FOR REMOTE DIGITAL TOWER
Remote digital towers (RDT) are taking place around the world to ensure efficiency and safety. TRUSTY harnesses the power of artificial intelligence (AI) to enhance resilience, capacity, and efficiency in making significant advan...
Remote digital towers (RDT) are taking place around the world to ensure efficiency and safety. TRUSTY harnesses the power of artificial intelligence (AI) to enhance resilience, capacity, and efficiency in making significant advancements in the deployment of digital towers. The overall goal of TRUSTY is to provide adaptation in the level of transparency and explanation to enhance the trustworthiness of AI-powered decisions in the context of RDT. Through the video transmission data from RDT, TRUSTY considers the following major tasks:
1. Taxiway monitoring (i.e., bird hazard, presence of a drone, autonomous vehicle monitoring, human intrusion, etc.).
2. Runway monitoring (approach and landing) misalignment warning and the corresponding explanation.
To deliver trustworthiness in an AI-powered intelligent system several approaches are considered:
• ‘Self-explainable and Self-learning’ system for critical decision-making
• ‘Transparent ML’ models incorporating interpretability, fairness, and accountability
• ‘Interactive data visualization and HMI dashboard’ for smart and efficient decision support
• ‘Adaptive level of explanation’ regarding the user's cognitive state.
• Human-centric AI enhances the trustworthiness of AI-powered systems.
• Human-AI teaming to consider users’ feedback to insure some computation flexibility and the users’ acceptability.
To achieve the goal, TRUSTY will rely on the SotA developments in interactive data visualization, and user-centric explanation and on recent technological improvements in accuracy, robustness, interpretability, fairness, and accountability. We will apply information visualization techniques like visual analytics, data-driven storytelling, and immersive analytics in human-machine interactions (HMI). Thus, this project is at the crossroad of trustworthy AI, multi-model machine learning, active learning, and UX for human and AI model interaction.
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Duración del proyecto: 32 meses
Fecha Inicio: 2023-06-14
Fecha Fin: 2026-02-28
Fecha Fin: 2026-02-28
Línea de financiación: concedida
El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-06-14
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
Presupuesto
El presupuesto total del proyecto asciende a
1,000K€
Líder del proyecto
MALARDALENS UNIVERSITET
No se ha especificado una descripción o un objeto social para esta compañía.
195.50K€ |
Participante
221.88K€ |
Participante