Building transparent and personalized AI support in air traffic control

Carl Westin, Brian Hilburn, Clark Borst, Erik Jan Van Kampen, Magnus Bang

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Artificial intelligence is considered a key enabler for realizing a more efficient future air traffic management system. As the automation designed to support us grows more sophisticated and complex, our understanding of it tends to suffer. Recent research has addressed this issue in two ways: either through increased automation transparency or increased personalization. This paper overviews recent work in these two areas of strategic conformance (i.e., personalization) and automation transparency (e.g., explainable artificial intelligence and machine learning interpretability). We discuss how to achieve and how to balance conformance and transparency in the context of a machine learning system for conflict detection and resolution in air traffic control. In the MAHALO project, we aim to build, and empirically evaluate, a personalized and transparent decision support system by combining supervised and reinforcement learning approaches. We believe that such a system could strive for optimal performance while accommodating individual differences. By knowing the individual's preferences, the system would be able to afford transparency by explaining both why it suggests another solution (that deviates from the individual's), and why this solution is considered to be better.

Original languageEnglish
Title of host publicationDASC 2020 - 39th Digital Avionics Systems Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)9781728198255
DOIs
Publication statusPublished - 2020
Event39th AIAA/IEEE Digital Avionics Systems Conference, DASC 2020 - Virtual, San Antonio, United States
Duration: 11 Oct 202016 Oct 2020

Publication series

NameAIAA/IEEE Digital Avionics Systems Conference - Proceedings
Volume2020-October
ISSN (Print)2155-7195
ISSN (Electronic)2155-7209

Conference

Conference39th AIAA/IEEE Digital Avionics Systems Conference, DASC 2020
CountryUnited States
CityVirtual, San Antonio
Period11/10/2016/10/20

Keywords

  • AI
  • ATM
  • Machine Learning
  • Personalized
  • Strategic conformance
  • Transparency

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