# Role of Multi-modal Machine Learning, Explainable AI and Human-AI Teaming in Trusted Intelligent Systems for Remote Digital Towers

Mobyen U Ahmed, Shaibal Barua, Shahina Begum, Waleed Jmoona, Ricky

S.D. Cruze, Alexandre Veyrie, Sara Kebir, Christophe Hurter

► To cite this version:

Mobyen U Ahmed, Shaibal Barua, Shahina Begum, Waleed Jmoona, Ricky S.D. Cruze, et al. Role of Multi-modal Machine Learning, Explainable AI and Human-AI Teaming in Trusted Intelligent Systems for Remote Digital Towers. AICCC 2024: 2024 the 7th Artificial Intelligence and Cloud Computing Conference, Jul 2025, Tokyo Japan, Japan. pp.26-35, 10.1145/3719384.3719389. hal-05176482

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<figure><img src="image_1.png" /></figure>
# Role of Multi-modal Machine Learning, Explainable AI and Human-AI Teaming in Trusted Intelligent Systems for Remote Digital Towers

Mobyen U. Ahmed*

School of Innovation Design and
Engineering
Malardalen University
Vasteras, Sweden
mobyen.uddin.ahmed@mdu.se

Shaibal Barua

School of Innovation Design and
Engineering
Malardalen University
Vasteras, Sweden
shaibal.barua@mdu.se

Shahina Begum

School of Innovation Design and
Engineering
Malardalen University
Vasteras, Sweden
shahina.begum@mdu.se

Waleed Jmoona

School of Innovation Design and
Engineering
Malardalen University
Vasteras, Sweden
waleed.jmoona@mdu.se

Ricky S.D Cruze

School of Innovation Design and
Engineering
Malardalen University
Vasteras, Sweden
ricky.stanley.d.cruze@mdu.se

Alexandre Veyrie

National School of Civil Aviation
Toulouse, France
alexandre.veyrie@enac.fr

Sara Kebir

National School of Civil Aviation
Toulouse, France
sara.kebir@enac.fr

Christophe Hurter

National School of Civil Aviation
Toulouse, France
christophe.hurter@enac.fr

## Abstract

Remote digital towers (RDTs) represent a transformative advance-
ment in air traffic management (ATM), leveraging cutting-edge
technology to enable remote operation by air traffic controllers
(ATCOs) while improving efficiency and safety. In the context of
RDTs, artificial intelligence (AI), Multimodal Machine Learning
(MML) and eXplainable AI (XAI) are playing an increasingly piv-
otal role in enhancing operational efficiency and safety. However,
several challenges need to be addressed, including the development
of AI, MML and XAI, research into functional requirements, and
the identification of inputs for user and machine interfaces, as well
as customization options. This study explores the use of XAI in
addressing specific air traffic control challenges and by offering
transparent, comprehensible, and actionable insights, XAI fosters
resilience, efficient, and closer collaboration between human oper-
ators and AI systems. Here, the study defines the specifications for
taxiway and runway monitoring and decision support within the
RDT domain. It outlines the functional requirements for customized
solutions, including XAI, human-centred XAI, human-machine in-
terfaces (HMI), and human-AI teaming (HAIT). A systematic litera-
ture review is conducted to assess transparency in AI, with a focus
on explainability, HMI, and graphical user interfaces (GUI) within

human-centred XAI for RDTs. Additionally, the research identi-
fies state-of-the-art techniques for interactive data visualization,
human-centric AI model development, hAIi interfaces, and HAIT,
providing a multi-modal agent framework for future development
in the RDT domain.

## CCS Concepts

* Computing methodologies; • Human-centered computing;

Keywords

eXplainable AI, human-machine interfaces (HMI), Human-AI team-
ing (HAIT), Remote digital towers (RDTs), Air traffic management
(ATM)

ACM Reference Format:

Mobyen U. Ahmed, Shaibal Barua, Shahina Begum, Waleed Jmoona, Ricky S.D Cruze, Alexandre Veyrie, Sara Kebir, and Christophe Hurter. 2024. Role of Multi-modal Machine Learning, Explainable AI and Human-AI Teaming in Trusted Intelligent Systems for Remote Digital Towers. In *2024 the 7th Artificial Intelligence and Cloud Computing Conference (AICCC) (AICCC 2024), December 14–16, 2024, Tokyo, Japan*. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3719384.3719389

# 1 Introduction

AI has become a transformative force across various sectors, includ-
ing aviation, where it promises to redefine air traffic control (ATC)
through RDT. The TRUSTY—Trustworthy Intelligent System for
Remote Digital Tower project<fnref n="1" /> exemplifies this innovation, aiming
to equip RDTs with AI to match the safety and efficiency of con-
ventional ATC towers. The field of air traffic management (ATM)

<figure><img src="image_2.png" /></figure>

<footnote>*corresponding author.</footnote>

<footnote n="1">¹https://research.dblue.it/trusty/</footnote>
has significantly evolved to meet the increasing demand for air travel. Key to this evolution is the use of multi-agent coordination techniques in ATM, which decentralizes system responsibilities for improved air traffic flow management [1]. The integration of automation, employing computers and flight management systems, aids human controllers in ATM [2]. The evolution and implementation of RDTs in the aviation industry have been explored in the context of the Single European Sky initiative [3]. The implementation of virtual views from the tower through multiple cameras increases the realism and detail of the airport environment for Air Traffic Control Officers (ATCOs) [4]. The application of augmented vision in remote towers was explored by [5], emphasizing the need for high-resolution digital panoramas.

MML and XAI are crucial for making complex AI systems transparent and understandable, fostering trust, acceptance and effective management in various sectors [6], [7]. The XAI agent for human-agent interaction explores the role of emotions in cognitive AI agents [8] and emphasizes responsible AI with fairness and accountability [9]. From a historical perspective, XAI evolved from expert systems to advanced machine-learning approaches aimed at developing human-understandable systems [10]. XAI applications show its expanding impact [11], supported by social science insights for effective human-like AI explanations [12], [13]. XAI is becoming increasingly pivotal in the field of ATM/ATC and, consequently, in RDT. The introduction of digital ATCOs, capable of autonomously performing time-consuming tasks, emphasizes the importance of a human-autonomy teaming interface, supported by XAI [14]. These advancements are crucial in making AI-driven decision-making in ATM/ATC, and RDT more transparent, reliable, and acceptable to human operators.

The objective of this study is to identify the definition, specification, and state-of-the-art (SoTA) of the Trustworthy Intelligent System (TIS) with the aim to provide a comprehensive overview of the current SoTA in AI systems and with a focus on their application to RDTs and the trustworthiness of AI. Here, the study explores these systems' theoretical underpinnings, practical implementations, and future directions, ensuring that the RDT project not only meets but exceeds the expectations of trustworthiness in intelligent systems. Again, to achieve the objectives, the study explores the multifaceted applications of XAI on several operational use case scenarios to enable a better understanding of user needs, i.e., ATCOs and Remote Towers Operators (RTOs). Based on the analysis, the study concluded with a concept of a multi-agent framework of a trusted intelligent system with four individual agents that cover several aspects encompassing Multimodal ML, robust and resilient ML, accountability, auditability, transparency, fairness, Human-Centred XAI (HCXAI) and human-machine teaming.

## 2 MATERIALS AND APPROACH

The study investigates and explores various ongoing projects in the field of ATM/ATC related to XAI, SoTA articles in trusted intelligent systems, and real-world operational scenarios in RDTs as materials. Here, the methodology follows a procedure that includes: a) **Selection of Materials**: Relevant materials were chosen based on their connection to trustworthy AI development, including existing projects, literature reviews, and industrial challenges.

b) **Formation of Analysis Team**: a team of several experts in AI, ethics, machine learning, data privacy, safety and verification was assembled. c) **Initial Review**: the selected materials were distributed among the team members, who conducted an initial review to identify key themes and concepts. d) **Categorization**: using a qualitative content analysis approach, the team members analysed the materials for recurring themes by discussing and refining them during regular meetings. e) **Data Extraction**: the content data were then extracted and categorized into different elements of trustworthy AI development, such as transparency, fairness, accountability, and robustness. f) **Synthesis of Findings**: the extracted elements were synthesized into a coherent framework involving cross-referencing. g) **Validation**: the outcome was reviewed by an external panel of experts in AI.

### 2.1 Projects in the field of ATM/ATC related to XAI

The survey [15] reported the advancement of XAI in ATM. Table 1 summarizes the prominent contributions of the research projects supported by SESAR JU within the ATM domain. In ATM/ATC, XAI represents a significant step in managing complex operations, aiding controllers with sophisticated pattern recognition, predictive analysis with data analytics and visualisation, and decision support. It enhances capabilities and provides a deeper understanding of AI-driven decisions, fostering trust and clarity among controllers. The integration of XAI into RDTs and ATC systems exemplifies a commitment to advancing aviation technology while ensuring transparency, understandability, and trustworthiness for human operators.

### 2.2 State-of-the-Art of trusted intelligent systems for RTDs

A literature study mainly on ongoing papers related to the development of Trustworthy AI is conducted for several reasons: 1) understanding the current research landscape 2) identifying key elements and trends, 3) recognizing gaps and opportunities, 4) building a foundation for analysis, 5) ensuring relevance and timeliness and 6) enhancing credibility and rigour. Here, a notable work focuses on enhancing the trustworthiness of AI systems by addressing critical aspects such as transparency, fairness, accountability, and robustness [16]. In the realm of AI for ATM/ATC, there is growing interest in applying these principles to systems such as RDTs. Here, research identifies ways to ensure AI systems are both explainable and trustworthy while being integrated into high-stakes environments like aviation [17]. The development of human-centred AI and AI transparency tools is crucial for enhancing human-machine teaming and decision-making. Other related articles are mentioned and discussed in section 3, "Trusted Intelligent Systems".

### 2.3 Real-world operational scenarios in RDTs

The section illustrates how XAI can significantly enhance decision-making processes, crisis management, and operational resilience in various challenging situations and explains the need for XAI for ATCOs and RTOs. The operational scenarios describe specific ATC challenges, which include crisis management, the impact of adverse conditions, and the integration of Unmanned Aerial Vehicles (UAVs).
<table><caption>**Table 1: Summary of the selected SESAR JU funded research projects contributing to the development of XAI in ATM/ATC.**</caption><thead><tr><td>Sl.</td><td>Project Name</td><td>Contribution</td></tr></thead><tbody><tr><td>1</td><td>AISA<sup>2</sup></td><td>Strategy for providing the necessary information to a specific ATM operational environment (en-route ATC) to make them trust the automated system.</td></tr><tr><td>2</td><td>MAHALO<sup>3</sup></td><td>AI-based Conflict Detection & Resolution tool with different levels of conformance and transparency.</td></tr><tr><td>3</td><td>TAPAS<sup>4</sup></td><td>XAI methods for two operational cases: Conflict Detection & Resolution applied to ATC (tactical), and Air Traffic Flow Management (ATFM) (pre-tactical).</td></tr><tr><td>4</td><td>ARTIMATION<sup>5</sup></td><td>Tools for Conflict Detection & Resolution and Delay Prediction with explanation through visualisations.</td></tr><tr><td>5</td><td>SAFEOPS<sup>6</sup></td><td>A decision-support tool powered by AI to help ATCOs make complex decisions in the context of go-arounds.</td></tr></tbody></table>

By providing ATCOs and RTOs with transparent, understandable, and actionable insights for such operations, XAI aims to introduce more resilience, efficiency, and willing collaboration between the end user and the AI. The scenarios are discussed below:

**Scenario 1: XAI-Driven Crisis Management in RDTs**<sup>7</sup>. Jean, an experienced air traffic controller, is currently overseeing operations from an RDT, now enhanced with XAI technology. In this airfield management scenario, Jean encounters an increase in aircraft flow, elevating the workload significantly. This surge presents Jean with unprecedented constraints, such as managing simultaneous take-offs and landings on runways. As the traffic volume intensifies and the queue of aircraft awaiting clearance grows, the role of the XAI system becomes indispensable. Distinct from traditional AI, the XAI system aids Jean by not only presenting data in an interactive and relevant way but also providing transparent explanations for its recommendations. For instance, it outlines why one aircraft is prioritized over another for landing based on factors like fuel levels, weather conditions, and emergency statuses, offering Jean a comprehensive understanding of the decision-making process behind traffic management.

**Scenario 2: XAI-Enabled Decision-Making Enhancement**<sup>8</sup>. Marie, an experienced ATCO, starts her shift at Muret-Lherm Aerodrome, which is now enhanced with RDT technology and integrated with XAI. Dense fog challenges the day, but initially, the RDT's advanced systems provide clear imagery despite the poor visibility. As fog causes condensation on camera lenses, impairing visibility, the XAI system becomes essential. Unlike traditional AI, XAI provides Marie not only with data but also with clear explanations for the aids to the decision-making process, crucial in managing air traffic under reduced visibility.

**Scenario 3: XAI-Enhanced Resilience in Aerodrome Operations**<sup>9</sup>. Raphaël, a skilled ATCO at London City Airport, starts his shift in a control tower enhanced with TRUSTY RDT technology, now integrated with explainable AI systems. This integration is designed to optimize routine ATM and provide robust responses to unforeseen challenges. A sudden power outage disrupts the control room, leading to a loss of visual monitoring capabilities. Despite the redundancy systems naturally present in the tower, the electrical incident affected multiple centres suspiciously, leaving only some functional services like XAI-based systems but cutting off video camera transmissions from all the runways.

**Scenario 4: XAI-Enhanced UAV Integration in Air Traffic Control**<sup>10</sup>. At the advanced regional ATC centre, the integration of XAI within the RDT systems marks the start of a routine yet technologically advanced day. The XAI system, with its capability to provide clear, understandable insights into AI decision-making processes, becomes crucial in managing a mix of flights and UAVs. When a group of UAVs dangerously enters a commercial flight corridor, the XAI system not only identifies this deviation but also provides the rationale behind flagging these UAVs as potential risks, along with suggested actions. The event takes an unexpected turn when the XAI system's data on the UAVs' positions conflicts with that shown by the surveillance cameras. This discrepancy presents a critical challenge, as relying on inaccurate data could lead to unsafe decisions. The XAI system, recognizing this anomaly, initiates a deeper analysis. It starts by cross-referencing the UAVs' past flight data with similar historical patterns and related atmospheric phenomena, aiming to reconcile the data mismatch.

**Scenario 5: Crisis Management in a Remote Digital Tower with UAVs, Weather, and System Failure.** During a challenging weather day, with a dense fog enveloping the area, both scheduled UAV flights and crewed aircraft operations persist. The severe conditions drastically reduce visibility, posing significant difficulties for Alex, the ATCO responsible for monitoring these flights in the aerodrome. The digital tower's cameras, designed to provide clear images of the airspace, struggle under these conditions, leading to a notable degradation of the approaching landings.

## 3 TRUSTED INTELLIGENT SYSTEM (TIS)

Trusted Intelligent System provides an in-depth analysis of the integration and impact of AI in ATM focusing on robust and interpretable AI, Human-Centric XAI, and Human Machine Teaming. To that end, it introduces a multi-agent framework with multimodality where humans can work collaboratively with multiple AI methods, tools and technologies, specifically adapted to provide specialised support for the various tasks within a TIS [18]. This agent-based approach will develop end-to-end policies and strategies (e.g., cooperative game theory and reinforcement learning-based strategies) for agent interaction, trust assessment, and formal specifications within multi-agent systems to enhance collaborative and trustworthy AI behaviours. Furthermore, agents must balance their objectives with the collective goal of trustworthy AI. Cooperative Game Theory and Reinforcement Learning based on team learning can help manage interactions between agents. Game theory-based

<footnote n="7"><sup>7</sup>Advanced Regional ATC Center, 2025.</footnote>

<footnote n="8"><sup>8</sup>Muret-Lherm Aerodrome with TRUSTY RDT Technology, France. 2025</footnote>

<footnote n="9"><sup>9</sup>London City Airport with TRUSTY RDT Technology, England. 2025.</footnote>

<footnote n="10"><sup>10</sup>Berlevag Aerodrome with TRUSTY RDT Technology, Norway. 2026</footnote>
<figure><img src="image_3.png" /><figcaption>Figure 1: Multimodal ML Agent in the context of RTDs.</figcaption></figure>

model interactions can make agents work together to achieve a shared goal while considering individual incentives. This includes designing mechanisms for fair distribution of rewards and aligning agents' strategies to promote cooperation. On the other hand, with team-based reinforcement learning, agents learn from both their own experiences and from interactions with other agents. This involves designing reward structures that incentivize collaborative behaviour and facilitate the convergence of individual strategies towards the collective goal. This approach ensures a team of specialized AI agents that complement each other for optimal success following three different modules to achieve TIS.

**Module 1: Communication and cooperation.** In TIS, multiple independent agents work together and communicate to achieve a common goal. Here, several considerations and challenges will be addressed, such as agents will trade-off and adapt to dynamic environments, robustness and uncertainty, and mitigate conflicting goals. The framework will also be able to handle the communication overhead and complexity of coordination among the agents. The agents will exchange information and collaborate to coordinate their actions effectively. Direct communication (e.g., Intelligent Physical Agents (FIPA) message-passing protocol) will be established to send messages explicitly between agents to share information, make requests, or negotiate. Further, to form coalitions and share the rewards among agents Game theoretical approaches (e.g., algorithms like the Shapley Value) will be considered.

**Module 2: Ensure Trust.** Two dimensions trust model such as 1) Trust among agents (machine-machine trust) i.e., Learned trust and Trust between human agents (human-machine Trust) i.e., Dispositional trust will be applied. For the learned trust, one of the indicators is that the outcomes of the system must be matched with real-world observations. To achieve this real-time data will be tracked and continuously compared with actual outcomes to achieve accuracy and reliability. The predictability of the system's

behaviour over time is also important to monitor. In TIS, analysis will be done to investigate how frequently an agent relies on the prediction and in what contexts. Dispositional trust will check the trust at both organisational and individual levels and be achieved by understanding how the AI system works. A trust scale, e.g., the Mayer, Davis, and Schoorman Trust Scale score will measure the human-to-machine trust besides the traditional surveys and questionnaire-based approach [19] [20].

**Module 3: Execute and evaluate decision.** In a multi-agent setting, complexity arises when the near-term actions of an agent impact the action of another agent. Hence, cooperation between multiple agents will be done using an execution protocol and evaluation policy to achieve joint objectives of multi-agents collectively over time as effectively as possible. TIS will employ decision theoretic multi-agent plan execution and evaluation, e.g., the Markov decision process (MDP) and partially observable MDP.

The four agents that represent four features of TIS are presented below:

i) **Multimodal ML Agent.** This agent is concerned with teaching computers to grasp and connect the information they receive from various senses like seeing, hearing, sensing and text. It's a crucial part of helping computers make sense of the world around us [21]. Here, different types of information will be provided for different tasks to understand this information in the context of the task at hand [22]. The RDT requires planning tools that support the controller with tasks such as sequencing flight movements, rearranging them according to situational demand, and organising remote controller positions, etc. Machine learning models developed to support these tasks can use data coming from different modalities, such as video streaming of visual surveillance, radar signals, communication through text messages, weather reports, and tabular data from air traffic management systems. In this AI agent, these different modalities can be fused and encompassed
<figure><img src="image_4.png" /><figcaption>Figure 2: Robust and resilient ML Agent.</figcaption></figure>

to provide holistic decisions that can reduce uncertainty and false
alarms and improve the trustworthiness of AI systems. Figure 1
illustrates the schematic of MML AI agents in the context of RTDs.

ii) **Robust and resilient ML Agent.** A good capability of an ML model is known as robustness and resilience, where the goal of this agent is to increase the robustness and resilience of the ML model by resisting error, outliers, and data distortion. It also involves the capacity to detect potential issues, make necessary adjustments, and continue functioning effectively when encountering unseen or unexpected noisy data. This robust and resilient ML agent could play a vital role in making judgments and predictions in crucial application areas like healthcare, banking, and transportation systems, such as ATMs. The diagram presented in Figure 2 illustrates the comprehensive process through which machine learning models demonstrate robustness and resilience in this AI agent. In our context, image classification tasks are essential for detecting objects in remote digital tower systems on runways and taxiways. Techniques include data augmentation, increasing the amount of labelled data, and employing various network architectures such as AlexNet, SqueezeNet, VGG-19, DenseNet-121, and ResNet-50 are applied here. However, it is important to note that while these techniques have the potential to improve the robustness of image classification, their effectiveness may vary across different cases [23].

iii) **Accountability & auditability Agent.** As Figure 3 shows accountability & auditability are increasingly important considerations in the development and deployment of AI systems. Ensuring accountability in AI systems means that there is a clear understanding of who is responsible for the actions taken by the system and that there are mechanisms in place to monitor and enforce compliance with ethical and legal standards. This agent introduces a methodology of fairness constraints for incorporating fairness constraints directly into ML [24]. It also provides the ability to assess and verify the performance and behaviour of an AI system, including its decision-making processes and outputs. The European Union's guidelines on Trustworthy AI emphasise the need for continuous monitoring and auditing of AI systems to ensure

compliance with fairness and non-discrimination principles<fnref n="11" />. Auditing tools for bias monitoring and mitigation still have several challenges the difficulty of defining and operationalising fairness, the trade-offs between fairness and model accuracy, and the need for interpretability in complex models.

iv) **XAI agent.** Researchers and practitioners are advancing the field by leveraging techniques like transformers and explainability frameworks, e.g. LIME and SHAP, along with interactive, personalized systems. Kulesza et al. (2015) on interactive ML highlights the importance of explanation systems [25]. Joshi et al. (2021) explore multimodal approaches to explanation generation [26]. XAI agents prioritise meeting the needs of users who rely on explanations [27], [28]. To achieve this, this agent should possess key attributes [29]:

* They must be understandable, catering to both experts and non-experts in AI. XAI aims to enhance transparency, necessitating explanations with accessible, commonly used terminology that is interpretable and intuitive.

* Actionable explanations empower users to make informed decisions when interacting with AI systems. These explanations should enable users to comprehend, trust, and effectively manage the AI system while generating high-quality, interpretable rationales for AI decisions.

* Trustworthiness is paramount, demanding accuracy and reliability in explanations. Grounding explanations in real-world examples and ensuring transparency in decision-making are essential to foster confidence among users. Given the inherent opacity of AI models, XAI seeks to alleviate trust challenges by providing explanations that bolster stakeholders' confidence in the utilisation of AI models.

**3.1 Human-AI Collaboration/Teaming**

It can be defined as the cooperative and coordinated interaction
between humans and AI systems to achieve a shared goal or task.
It involves the integration of human and AI capabilities, where hu-
mans and AI systems work together as interdependent teammates

<footnote n="11">¹¹European Commission. (2019). Ethics Guidelines for Trustworthy AI. European Commission</footnote>
<figure><img src="image_5.png" /><figcaption>Figure 3: Accountability & auditability Agent</figcaption></figure>

to leverage their respective strengths and expertise [30]. This collaboration can take various forms, such as humans delegating tasks to AI systems, AI systems providing recommendations or assistance to humans, or humans and AI systems jointly making decisions [31]. The goal of human-AI collaboration is to enhance decision-making, problem-solving, and creative processes by combining the strengths of humans and AI systems. It aims at leveraging the computational power, data processing capabilities, and pattern recognition abilities of AI systems, along with the human abilities of critical thinking, creativity, and contextual understanding [32]. Human-AI teaming is being explored in various applications across diverse domains: In military operations, AI is integrated into autonomous systems for target recognition, navigation, and situational awareness, while humans provide strategic guidance and ethical oversight [33], [34]. Moreover, in healthcare, AI assists in medical diagnosis, treatment planning, and patient monitoring, while humans provide expertise and empathy [35], [36]. Additionally, in ATM [37], [38], where AI has the potential to enhance operational efficiency, careful consideration of factors such as team composition, communication, trust, and the impact of AI on human design teams is essential to ensuring effective human-AI collaboration in ATM. In general, Human-AI teaming is particularly valuable in complex and data-intensive domains, where AI systems can analyse large amounts of data and provide insights, while humans can provide domain expertise, interpret the results, and make informed decisions [31].

# 4 CHALLENGES IN TRUSTWORTHY INTELLIGENT SYSTEM

Several challenges for the Trustworthy Intelligent System (TIS) have been identified based on the mentioned ongoing projects, SotA and real-world operational scenarios in RDTs

## 4.1 Challenges in MML

As the data are coming from different sources like images, sound, video, text and sensor readings, it brings about unique and tricky issues for the ML community. To tackle these challenges, the paper introduces a system of six main technical problems: representation, alignment, reasoning, generation, transference, and quantification [22]. A different study [21] explores more practical considerations

for the application of MML. This study looks at what can be done with MML, like recognising speech, describing events, answering questions about images, and finding media in large collections. However, it highlights the technical hurdles to overcome. These challenges are about how to represent and translate the different types of data, align them properly, merge them, and learn from them, especially when one data type doesn't contain much information.

## 4.2 Challenges in fairness

The paper [39] identifies four key challenges in ML model fairness research:

1. Balancing the trade-off between fairness and model performance, where to consider the social and cultural factors in ML.

2. (Dis)agreement and Incompatibility of “Fairness”, there’s a debate in the literature about whether it’s more important to be fair to individuals or groups when using ML models. Fairness metrics usually focus on one or the other but not both. Some methods that aim to make things fair for groups can make things worse for individuals within those groups. Also, the way fairness is defined mathematically doesn’t always match how society, economics, or the law see it.

3. Tensions with Context and Policy, the current research on fairness in ML often tries to make things fair without really understanding the root causes of unfairness that can lead to bias. The data used to train the ML models often reflects past biases and may not accurately represent the real world.

4. The democratisation of ML vs the Fairness Skills Gap, nowadays ML is more accessible with many tools and cloud-based solutions. This democratization of ML can lead to both positive and unintended, socially insensitive uses. However, the challenge is that addressing bias and fairness in machine learning requires expertise, and there’s a gap in tools and resources for those without extensive technical knowledge.

## 4.3 Challenges and Advancements in Human-Centred XAI

The authors in [40], propose to address this challenge by focusing on the explainability of simple mathematical models and assessing
<figure><img src="image_6.png" /><figcaption>Figure 4: Example of Human-Centred XAI.</figcaption></figure>

<table><caption>Table 2: Challenges of Human-AI Collaboration.</caption><thead><tr><td>Challenges</td><td>Category</td><td>Description</td><td>Source</td></tr></thead><tbody><tr><td>Design and Interaction</td><td>Technical</td><td>Designing effective and user-friendly human-AI interactions is difficult due to the uncertainty surrounding AI's capabilities and the complexity of AI outputs.</td><td>[43]</td></tr><tr><td>Social and Behavioural</td><td>Social</td><td>Social and behavioural factors, such as trust and confidence in AI systems, can influence the adoption of human-AI collaboration.</td><td>[35], [44]</td></tr><tr><td>Trust and Confidence</td><td>Technical</td><td>Establishing trust and confidence in AI systems is crucial for effective human-AI collaboration.</td><td>[30], [45]</td></tr><tr><td>Ethical and Societal</td><td>Ethical</td><td>Human-AI collaboration raises ethical and societal concerns, such as bias and discrimination.</td><td>[46], [47]</td></tr><tr><td>Integration and Coordination</td><td>Technical</td><td>Integrating and coordinating human and AI capabilities can be challenging due to the need to optimise AI for teamwork and manage HMI interfaces.</td><td>[48], [49]</td></tr></tbody></table>

how people perceive the comprehensibility of different model representations. XAI for smart cities is a relevant area of research that can provide insights into the development of HCXAI in DRT. This includes addressing technical challenges, ensuring transparency and accountability, and leveraging XAI techniques to improve decision-making processes [41]. Furthermore, the application of XAI in the prognostic and health management (PHM) of industrial assets can provide valuable insights for human-centred XAI in digital remote towers. The balance between accuracy and explainability is a key consideration in PHM, and similar trade-offs may need to be made

in the context of DRT. The involvement of human operators, explanation assessment, and uncertainty quantification are important factors to consider in developing XAI solutions for PHM, and these insights can be applied to the development of HCXAI in digital remote towers [42].

## 4.4 Challenges in Human Machine Teaming

While human-AI teaming holds immense promise, it also presents several challenges, as in Table 2.
<table><caption>Table 3: List of categories and methods</caption><thead><tr><th>Category</th><th>Method</th><th>Application</th><th>Paper</th></tr></thead><tbody><tr><td rowspan="2">Image and vision</td><td>Convolutional Neural Networks (CNNs)</td><td>Image captioning, visual question-answering, and multimedia</td><td>[21]</td></tr><tr><td>Convolutional Neural Networks (CNNs), event detection.</td><td></td><td></td></tr><tr><td rowspan="3">Text</td><td>Generative Adversarial Networks (GANs)</td><td>Visual Question Answering (VQA), Visual Commonsense Reasoning, Visual Dialogue, Phrase Grounding</td><td>[22]</td></tr><tr><td></td><td>Image classification, object detection, image captioning, and visual question answering</td><td>[50]</td></tr><tr><td>Recurrent Neural Networks (RNNs) for NLP</td><td>Attention mechanisms in generating image captions, deep learning for learning image-text embeddings.</td><td>[21]</td></tr><tr><td rowspan="3">MML</td><td>NLP</td><td>Visual Question Answering (VQA), Visual Commonsense Reasoning, Visual Dialogue, Phrase Grounding</td><td>[22]</td></tr><tr><td>Recurrent Neural Networks (RNNs) and Transformer models, Word embeddings (e.g., Word2Vec, GloVe)</td><td>Sentiment analysis, machine translation, text summarization, and document classification</td><td>[50]</td></tr><tr><td>Multimodal Knowledge Graphs, Multimodal Commonsense Reasoning, Attention Mechanisms, Graph-based Methods, generative adversarial networks</td><td>Event detection</td><td>[21] [51]</td></tr><tr><td rowspan="2">Fairness</td><td>Methods, clustering</td><td>Visual question answering, knowledge base completion, image captioning, recommendation systems and data</td><td>[50]</td></tr><tr><td>Methods, clustering</td><td>Methods, generative adversarial networks</td><td>[50]</td></tr><tr><td>Fairness</td><td>Methods, clustering</td><td>Methods, clustering</td><td>[39]</td></tr><tr><td></td><td>Methods, clustering</td><td>Methods, clustering</td><td>[39]</td></tr><tr><td></td><td>Methods, clustering</td><td>Methods, clustering</td><td>[39]</td></tr><tr><td></td><td>Methods, clustering</td><td>Methods, clustering</td><td>[39]</td></tr></tbody></table>

# 5 Discussion

The study explores the challenges and investigates the role of AI, MML, XAI, and human-AI teaming. It focuses on the scenarios based on RDTs operations as identified and discussed in this paper. Also, it considers MML models that incorporate explainability, fairness, auditability and accountability of AI systems. To make the AI system transparent, one important aspect is that the TIS should consider human-machine teaming so that the AI system is trustworthy by design. Again, in a multi-modal agent-based framework, it proposed advanced robust and interpretable AI, HCXAI and HAIT by integrating key learnings from the challenges and recent developments in the field. It seeks to foster trust, understanding, and effective collaboration between humans and AI systems, focusing on RDT in the context of ATMs. This SotA report lays the foundation for the technical roadmap and future directions of the TIS as summarised in Table 3.

The foundation of the current RDT project should consist of robust and resilient ML models that are carefully designed to withstand data anomalies and maintain optimal performance. Robustness is crucial, considering the high stakes involved and the profound consequences of any potential failure. Simultaneously, accountability and auditability are essential because they offer a structure for traceability and compliance with legal and ethical requirements, all of which are necessary to maintain public confidence and guarantee regulatory compliance. Furthermore, the principles of transparency and fairness are integral for RDT, as they are instrumental in eliminating biases and guaranteeing that AI systems operate with unequivocal clarity and impartiality, thereby ensuring equitable and unbiased decision-making that treats every aircraft and operator with the same level of objectivity. HCXAI prioritizes the delivery of explanations that are understandable and actionable,

fostering trust and enabling effective human oversight of AI systems. The HCXAI is particularly relevant for the RDT project, where operators must fully grasp AI-generated advice to make informed decisions. Furthermore, human-machine teaming encapsulates the collaborative, constructive collaboration between human operators and AI systems, leveraging their combined strengths to optimize air traffic management in the RDT project.

# 6 Conclusion

The object of this study is to define the specifications regarding trustworthy AI solutions in the operational activities in RDT, especially in runway and taxiway monitoring. Here, the paper presents a general background of related work in the RDTs, defines some use-case scenarios, and delves into the various aspects of trustworthy AI and machine learning. It also provides an overview of human-machine teaming to understand the effects of HCAI in RDTs' operations. Here the study identified multi-modal machine learning, explainable AI and human-AI teaming in TIS for remote digital towers, as recommendations for building TIS:

* Large language models (LLM) technology in an RDT can improve aviation operations and safety. However, LLM should be tailored for specific use case, e.g., communication between controllers and pilots.

* Multi-modal AI/ML can improve the question-answering system, such as answering questions from controllers or pilots about weather, flight schedules, or other important information. Multimodality not only improves the performance of the system but also can improve reliability as data will come from different sources.

* Reflective Human-Centred XAI (HCXAI), which takes a sociotechnical informed perspective on XAI can increase trust
in the AI system. XAI developers should consider interactive and on-demand explanations that account for the diversity of human thoughts and experiences.

* Generative AI can be used to produce human-readable explanations as evidence for AI decision-making, considering diverse evidence generation.

* Agent-based approach can be used for cooperative and coordinated interaction between humans and AI systems to achieve shared goals or tasks.

## Acknowledgments

This study is part of the project 1) TRUSTY (Trustworthy Intelligent System for Remote Digital Tower) funded by SESAR 3 Joint Undertaking under grant agreement No 101114838; 2) FitDrive (This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 953432); and 3) CPMXai (Cognitive Predictive Maintenance and Quality Assurance using Explainable AI and Machine Learning, under Project 2021-03679, and funded by VINNOVA, Sweden's Innovation Agency. Also, the authors would like to acknowledge Pietro Arico', Giulia cartocci, Mir Riyanul Islam and Elizabeth Humm for reviewing the text.

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