In the emerging age of Artificial Intelligence and Machine Learning, the MAHALO SESAR Exploratory Research project aims to answer simple, yet profound questions: should we be developing automation that is conformal to the human, or should we be developing automation that is transparent to the human? Do we need both? Further, are there tradeoffs / interactions between the concepts, in terms of air traffic controller trust, acceptance, or performance?

The MAHALO consortium, consisting of DeepBlue, Center for Human Performance Research (CHPR), TU Delft (TUD), Linkoping University (LiU) and Air Navigation Services of Sweden (LFV), will develop and empirically evaluate the next generation computer-based tools supporting the transition to higher levels of automation in air traffic control.

The technologies being developed include the combination of (supervised & unsupervised) Machine Learning models with Ecological Interface Design to create transparent & explainable AI solutions that can provide both individualized and optimal support to human air traffic controllers.

For more information, please visit:

http://mahaloproject.eu/