In Air Transportation Management the Decision Making Process is already associated with AI. The algorithms are meant to help ATCOs in daily tasks, but they still face acceptability issues. Today’s automation systems with AI/Machine Learning do not provide additional information on top of the Data Processing result to support its explanation, making them not transparent enough. The Decision Making Process is expected to become a “White Box”, giving understandable outcome through an understandable process.
ARTIMATION’s goal is providing a transparent and explainable AI model through visualization, data driven storytelling and immersive analytics. This project will take advantage of human perceptual capabilities to better understand AI algorithm with appropriated data visualization as a support for explainable AI (XAI), exploring in the ATM field the use of immersive analytics to display information
ARTIMATION will end after December 2022, and it is now time to disseminate every project concept and outcome to the advisory board and to everyone
If the Conflict Detection and Resolution use case was focused on an explanation of the AI outcome provided through the interface, the Delay Prediction use
During the first year and a half of the project, we introduced many times the task of Conflict Detection and Resolution. After the Advisory Board
This project has received founding from the SESAR Joint Undertaking grant agreement No. 894238 under European Union’s Horizon 2020 research and innovation programme
Project coordinator
Mälardalen University
Mobyen Uddin Ahmed
mobyen.ahmed@mdh.se
The opinions expressed herein reflect the author’s view only. Under no circumstances shall the SESAR Joint Undertaking be responsible for any use that may be made of the information contained herein.