In short




2020-06-01 > 2022-11-30


EUR 997 212,50


EUR 997 212,50




MAHALO asks a simple but profound question: in the emerging age of Machine Learning (ML), should we be developing automation that matches human behavior (i.e., conformal), or automation that is understandable to the human (i.e., transparent)? Further, what tradeoffs exist, in terms of controller trust, acceptance, and performance? To answer these questions, MAHALO is:

  • Developing an individually-tuned ML system comprised of layered deep learning and reinforcement models, trained on controller performance (context-specific solutions), strategies (eye tracking), and physiological data, which learns to solve ATC conflicts;
  • Couple this to an enhanced en-route CD&R prototype display to present machine rationale with regards to ML output;
  • Evaluating in realtime simulations the relative impact of ML conformance, transparency, and traffic complexity, on controller understanding, trust, acceptance, workload, and performance; and
  • Defining a framework to guide design of future AI systems, including guidance on the effects of conformance, transparency, complexity, and non-nominal conditions.

Building on the collective experience within the team, past research, and recent advances in the areas of ML and ecological interface design (EID), MAHALO takes a bold step forward: to create a system that learns from the individual operator, but also provides the operator insight into what the machine has learnt. Several models are being trained and evaluated to reflect a continuum from individually-matched to group-average. Most recent work in areas of automation transparency, Explainable AI (XAI) and ML interpretability is being explored to afford understanding of ML advisories. The user interface present ML outputs, in terms of: current and future (what-if) traffic patterns; intended resolution maneuvers; and rule-based rationale. The project’s output adds knowledge and design principles on how AI and transparency can be used to improve ATM performance, capacity, and safety.


HPR Center for Human Performance Research BV

Deep Blue SRL (Coordinator)

Linköping University


Technische Universiteit Delft

This project has received funding from the SESAR Joint Undertaking under the European Union's Horizon 2020 research and innovation programme under grant agreement No 892970

European Union