An Explainable Artificial Intelligence (xAI) Framework for Improving Trust in Automated ATM Tools

Hernandez, Carolina Sanchez; Ayo, Samuel; Panagiotakopoulos, Dimitrios

doi:10.1109/dasc52595.2021.9594341

Cited by 10 publications

(2 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite the extensive research on these technologies, a gap exists between the research outcomes and practical implementation due to regulatory and practical challenges, including the need for transparency and explainability. To address these challenges, a novel framework is proposed to instill trust in AI-based automated solutions, drawing from the current guidelines and end-user feedback [88]. Recommendations are provided to facilitate the adoption of AI-and MLbased solutions in ATM, leveraging the framework to bridge the gap between research and implementation.…”

Section: Aviationmentioning

confidence: 99%

Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications

Sharma,

Chand,

Buksh

et al. 2024

Algorithms

View full text Add to dashboard Cite

This study delves into the realm of Explainable Artificial Intelligence (XAI) frameworks, aiming to empower researchers and practitioners with a deeper understanding of these tools. We establish a comprehensive knowledge base by classifying and analyzing prominent XAI solutions based on key attributes like explanation type, model dependence, and use cases. This resource equips users to navigate the diverse XAI landscape and select the most suitable framework for their specific needs. Furthermore, the study proposes a novel framework called XAIE (eXplainable AI Evaluator) for informed decision-making in XAI adoption. This framework empowers users to assess different XAI options based on their application context objectively. This will lead to more responsible AI development by fostering transparency and trust. Finally, the research identifies the limitations and challenges associated with the existing XAI frameworks, paving the way for future advancements. By highlighting these areas, the study guides researchers and developers in enhancing the capabilities of Explainable AI.

show abstract

Section: Aviationmentioning

confidence: 99%

Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications

Sharma,

Chand,

Buksh

et al. 2024

Algorithms

View full text Add to dashboard Cite

show abstract

“…There is a gap between research projects and their practical implementation due to different policy and practical challenges. To help overcome these challenges, frameworks have been developed to build trust and enable end-user feedback [12]. The aim is that these frameworks help to advance AI within ATM and reduce the gap between research and implementation of this technology.…”

Section: Introduction 1background and Related Workmentioning

confidence: 99%

Study of the Impact of Traffic Flows on the ATC Actions

et al. 2022

View full text Add to dashboard Cite

It has always been a topic of great interest in air transport management to be able to estimate controller workload. So far, research has not had the opportunity to make use of real data on the controller’s actions. We have enough data to be able to use machine learning methods. The aim of this work is to predict the controller’s actions to know his workload. Several machine learning models were tested to try different combinations of features and the selected algorithms and two models were finally chosen. The predictions provided by the models are good enough to be used when a first approximation of the workload in a sector is to be obtained. Finally, explainability techniques were employed to discover the patterns found by the AI in the machine learning models. Thanks to these techniques, we can build a profile of the critical flights that increase the workload the most.

show abstract