TRUSTY: TRUSTWORTHY INTELLIGENT SYSTEM FOR REMOTE DIGITAL TOWER

Status:

active

Start date:

2023-09-01

End date:

2026-02-28

Overall, the goal of TRUSTY is to provide adaptation in the level of transparency to enhance the trustworthiness of AI-powered decisions in the context of remote digital towers (RDTs). While in an actual tower, operators have direct visual access to the taxiway and runway monitoring, the RDTs concept only provides such information through video transmission with a warning and the corresponding explanation. To deliver trustworthiness in an AI-powered intelligent system TRUSTY will consider several approaches, and they are listed:
•    ‘Self-explainable and Self-learning’ system for critical decision-making
•    ‘Transparent ML’ models incorporating interpretability, fairness, and accountability
•    ‘Interactive data visualization and multimodal human-machine interface/interactions (HMI), i.e., Graphical User Interface (GUI)’ for smart and efficient decision support
•    ‘Adaptive level of explanation’ regarding the user's cognitive state.
•    “HCAI” to enhance the trustworthiness of AI-powered systems.
•    “Human-machine collaboration (HMC) or Human-AI teaming (HAIT)” to consider user feedback to insure some computation flexibility and the users’ acceptability.

[Show all publications]

Role of Multi-modal Machine Learning, Explainable AI and Human-AI Teaming in Trusted Intelligent Systems for Remote Digital Towers (Jan 2025)
Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum, Waleed Reafee Sbu Jmoona, Ricky Stanley D Cruze , Alexandre Veyrie , Christophe Hurter
7th Artificial Intelligence and Cloud Computing Conference (AICCC2024)

Research Issues and Challenges in the Computational Development of Trustworthy AI (Aug 2024)
Shahina Begum, Mobyen Uddin Ahmed, Shaibal Barua, Md Alamgir Kabir
IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET 2024)

Balancing Fairness: Unveiling the Potential of SMOTE-Driven Oversampling in AI Model Enhancement (May 2024)
Md Alamgir Kabir , Mobyen Uddin Ahmed, Shahina Begum, Shaibal Barua, Md Rakibul Islam
International Conference on Machine Learning Technologies (ICMLT)

Examining Decision-Making in Air Traffic Control: Enhancing Transparency and Decision Support Through Machine Learning, Explanation, and Visualization: A Case Study (Mar 2024)
Christophe Hurter , Augustin Degas , Arnaud Guibert , Maelan Poyer , Nicolas Durand , Alexandre Veyrie , Ana Ferreira , Stefano Bonelli , Mobyen Uddin Ahmed, Waleed Reafee Sbu Jmoona, Shaibal Barua, Shahina Begum, Giulia Cartocci , Gianluca Di Flumeri , Gianluca Borghini , Fabio Babiloni , Pietro Aricò
16th International Conference Agents and Artificial Intelligence (ICAART2024)

Mobyen Uddin Ahmed, Professor

Email: mobyen.uddin.ahmed@mdu.se
Room: U1-089
Phone: +46-021-107369