Examining Decision-Making in Air Traffic Control: Enhancing Transparency and Decision Support Through Machine Learning, Explanation, and Visualization: A Case Study Article Swipe
Christophe Hurter
,
Augustin Degas
,
Arnaud Guibert
,
Maëlan Poyer
,
Nicolas Durand
,
Alexandre Veyrie
,
Ana Ferreira
,
Nicola Cavagnetto
,
Stefano Bonelli
,
Mobyen Uddin Ahmed
,
Waleed Jmoona
,
Shaibal Barua
,
Shahina Begum
,
Giulia Cartocci
,
Gianluca Di Flumeri
,
Gianluca Borghini
,
Fabio Babiloni
,
Pietro Aricò
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5220/0012471900003636
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5220/0012471900003636
Artificial Intelligence (AI) has recently made significant advancements and is now pervasive across various \napplication domains. This holds true for Air Transportation as well, where AI is increasingly involved in \ndecision-making processes. While these algorithms are designed to assist users in their daily tasks, they still \nface challenges related to acceptance and trustworthiness. Users often harbor doubts about the decisions \nproposed by AI, and in some cases, they may even oppose them. This is primarily because AI-generated \ndecisions are often opaque, non-intuitive, and incompatible with human reasoning. Moreover, when AI is \ndeployed in safety-critical contexts like Air Traffic Management (ATM), the individual decisions generated \nby AI models must be highly reliable for human operators. Understanding the behavior of the model and \nproviding explanations for its results are essential requirements in every life-critical domain. In this scope, this \nproject aimed to enhance transparency and explainability in AI algorithms within the Air Traffic Management \ndomain. This article presents the results of the project’s validation conducted for a Conflict Detection and \nResolution task involving 21 air traffic controllers (10 experts and 11 students) in En-Route position (i.e. hight \naltitude flight management). Through a controlled study incorporating three levels of explanation, we offer \ninitial insights into the impact of providing additional explanations alongside a conflict resolution algorithm \nto improve decision-making. At a high level, our findings indicate that providing explanations is not always \nnecessary, and our project sheds light on potential research directions for education and training purposes.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5220/0012471900003636
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392305769
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392305769Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5220/0012471900003636Digital Object Identifier
- Title
-
Examining Decision-Making in Air Traffic Control: Enhancing Transparency and Decision Support Through Machine Learning, Explanation, and Visualization: A Case StudyWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Christophe Hurter, Augustin Degas, Arnaud Guibert, Maëlan Poyer, Nicolas Durand, Alexandre Veyrie, Ana Ferreira, Nicola Cavagnetto, Stefano Bonelli, Mobyen Uddin Ahmed, Waleed Jmoona, Shaibal Barua, Shahina Begum, Giulia Cartocci, Gianluca Di Flumeri, Gianluca Borghini, Fabio Babiloni, Pietro AricòList of authors in order
- Landing page
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https://doi.org/10.5220/0012471900003636Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
-
https://hal.science/hal-04739928Direct OA link when available
- Concepts
-
Transparency (behavior), Visualization, Computer science, Decision support system, Air traffic control, Control (management), Machine learning, Artificial intelligence, Data science, Human–computer interaction, Engineering, Computer security, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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