Artificial Intelligence (AI) has recently improved by leaps and bounds and is now present in every application domain. This is also the case for Air Transportation, where decision making is more and more associated with AI and in particular with Machine Learning (ML). While these algorithms are meant to help users in their daily tasks, they still face acceptability issues. Users are doubtful about the proposed decision or even worse opposed to it since the decision provided by AI is most of the time opaque, non-intuitive and not understandable by a human. So, compared to a natural discussion between two users, the machines often provide information without the opportunity to justify it. In other words, today’s automation systems with AI or ML do not provide additional information on top of the data processing result to support its explanation which makes them not transparent enough. Also, when AI is applied in a high-risk context such as Air Traffic Management (ATM) individual decision generated by the AI model should be trusted by the human operators. Understanding the behaviour of the model and explanation of the result is a necessary condition for trust. To address these limitations, the ARTIMATION project investigates the applicability of AI methods from the domain of Explainable Artificial Intelligence (XAI). In the project, we will investigate specific features to make AI model transparent and post hoc interpretable (i.e., decision understanding) for users in the domain of ATM systems.
|First Name||Last Name||Title|
|Mir Riyanul||Islam||Doctoral student|
When a CBR in Hand is Better than Twins in the Bush (Sep 2022) Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum, Mir Riyanul Islam, Rosina O Weber Fourth Workshop on XCBR: Case-Based Reasoning for the Explanation of Intelligent Systems (XCBR)
A Systematic Review of Explainable Artificial Intelligence in terms of Different Application Domains and Tasks (Jan 2022) Mir Riyanul Islam, Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum Applied Sciences - Computing and Artificial Intelligence (Special Issue: Explainable Artificial Intelligence (XAI)) (ApplSci XAI)
A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory (Jan 2022) Augustin Degas , Mir Riyanul Islam, Christophe Hurter , Shaibal Barua, Hamidur Rahman, Minesh Poudel , Daniele Ruscio , Mobyen Uddin Ahmed, Shahina Begum, Md Aquif Rahman, Stefano Bonelli , Giulia Cartocci , Gianluca Di Flumeri , Gianluca Borghini , Fabio Babiloni , Pietro Aricò Applied Sciences - Computing and Artificial Intelligence (Special Issue: Explainable Artificial Intelligence (XAI)) (ApplSci XAI)