Preflight planning for large-scale Unmanned Aerial Vehicle (UAV) fleets in dynamic, shared airspace presents significant challenges, including temporal No-Fly Zones (NFZs), heterogeneous vehicle profiles, and strict delivery deadlines. While Multi-Agent Path Finding (MAPF) provides a formal framework, existing methods often lack the scalability and flexibility required for real-world Unmanned Traffic Management (UTM). We propose DTAPP-IICR: a Delivery-Time Aware Prioritized Planning method with Incremental and Iterative Conflict Resolution. Our framework first generates an initial solution by prioritizing missions based on urgency. Secondly, it computes roundtrip trajectories using SFIPP-ST, a novel 4D single-agent planner (Safe Flight Interval Path Planning with Soft and Temporal Constraints). SFIPP-ST handles heterogeneous UAVs, strictly enforces temporal NFZs, and models inter-agent conflicts as soft constraints. Subsequently, an iterative Large Neighborhood Search, guided by a geometric conflict graph, efficiently resolves any residual conflicts. A completeness-preserving directional pruning technique further accelerates the 3D search. On benchmarks with temporal NFZs, DTAPP-IICR achieves near-100% success with fleets of up to 1,000 UAVs and gains up to 50% runtime reduction from pruning, outperforming batch Enhanced Conflict-Based Search in the UTM context. Scaling successfully in realistic city-scale operations where other priority-based methods fail even at moderate deployments, DTAPP-IICR is positioned as a practical and scalable solution for preflight planning in dense, dynamic urban airspace.

The Strategic Research, Innovation, and Deployment Agenda (SRIDA) is a defining framework for the AI, Data, and Robotics Association (Adra) and a forward-looking roadmap for the advancement of AI, data, and robotics (ADR) in Europe. It aims to align ADR research and development efforts, address societal challenges, enhance economic competitiveness, and inform the European Commission’s Horizon Europe work program. This chapter outlines the SRIDA’s purpose and the stakeholders involved and proposes a collaborative methodology based on our practical experience preparing the SRIDA for the past 3 years. It describes the operational framework, including tools, contribution processes, development phases, and iterative updates that ensure the SRIDA evolves in alignment with diverse priorities and voices. The SRIDA’s dynamically evolving nature reflects its commitment to transparency, inclusivity, and balancing technical and non-technical goals. Building on previous SRIDA editions, the chapter examines constraints such as balancing agendas, fostering equitable representation within the ADR community, and aligning immediate objectives with long-term strategies. By addressing open challenges and providing actionable recommendations for future SRIDA iterations, this chapter demonstrates SRIDA’s potential as a driver of Europe’s ADR leadership, paving the way for sustainable innovation.

This chapter explores the geopolitics of AI governance from a sustainability perspective. The governance of AI is increasingly hybrid: there is a convergence on some principles and agreed upon challenges. In parallel, we witness the emergence of legally binding instruments, complemented by soft laws. We also analysed a selection of four international organisations’ AI strategies and three national strategies. Concerns identified are the need for common rules precise enough for implementation; navigating risks and benefits in the short and long term; and balancing technological sovereignty and the inherent need for collaboration in AI development. Despite some positive efforts, the current AI governance landscape does not foster sustainability. Ways forward include making large language models a public good accessible to all, supported by the public sector, as well as mainstreaming sustainability approaches in AI development.

Explainable artificial intelligence (XAI) and fair learning have made significant strides in various application domains, including criminal recidivism predictions, healthcare settings, toxic comment detection, automatic speech detection, recommendation systems, and image segmentation. However, these two fields have largely evolved independently. Recent studies have demonstrated that incorporating explanations into decision-making processes enhances the transparency and trustworthiness of AI systems. In light of this, our objective is to conduct a systematic review of FairXAI, which explores the interplay between fairness and explainability frameworks. To commence, we propose a taxonomy of FairXAI that utilizes XAI to mitigate and evaluate bias. This taxonomy will be a base for machine learning researchers operating in diverse domains. Additionally, we will undertake an extensive review of existing articles, taking into account factors such as the purpose of the interaction, target audience, and domain and context. Moreover, we outline an interaction framework for FairXAI considering various fairness perceptions and propose a FairXAI wheel that encompasses four core properties that must be verified and evaluated. This will serve as a practical tool for researchers and practitioners, ensuring the fairness and transparency of their AI systems. Furthermore, we will identify challenges and conflicts in the interactions between fairness and explainability, which could potentially pave the way for enhancing the responsibility of AI systems. As the inaugural review of its kind, we hope that this survey will inspire scholars to address these challenges by scrutinizing current research in their respective domains.

National and international policy documents emphasize the need for AI-related competencies “for all”, but there is little clarity on what these competencies should include, and determining what non-experts need to know remains a challenge. AI literacy has become a widely discussed topic in this context, often referring to a set of skills that empower individuals to critically evaluate AI, communicate and collaborate effectively with AI systems, and utilize AI as a tool across diverse contexts, including online environments, homes, schools, and workplaces. However, what AI literacy looks like in practice depends on factors such as age, level of education, and individual background. In this article, we frame AI literacy based on a qualitative analysis of the views of 33 international experts from various disciplines on what AI literacy in K-12 education should encompass. This analysis builds on existing AI literacy frameworks, with a focus on understanding and critically evaluating AI’s role in daily life, recognizing and using AI, and designing AI solutions for everyday problems. The findings show that experts emphasize a wide range of knowledge, skills, and attitudes, highlighting the importance of multiple perspectives when exploring this emerging field.

AI literacy in school education is booming within the scientific discourse of AI in education. How AI literacy is currently being framed serves diverse educational, political, and commercial purposes influencing how we imagine postdigital classrooms today and in the future. More importantly, how AI literacy emerges in primary education notably impacts how children understand AI and their own agency in a society where AI is ubiquitous. This study reviews how scientific literature conceptualises AI literacy, focusing on middle school students. An AI-adapted literacy framework (GeST) is used in the analysis to distinguish three perspectives of AI literacy (Generic, Situated, and Transformative). Forty-four papers from 2016–2024 were included in the final descriptive and qualitative analysis, showing an exponential growth in scientific papers. While still vaguely defined and poorly theorised, AI literacy materialises into different AI curricula and technology-supported teaching activities. The GeST analysis indicates that AI literacy is primarily viewed as a set of measurable skills related to generalisable theoretical knowledge that is expected to make children more competitive in a globalised and technologised world. Although some papers consider empowering students with specific competencies to challenge the AI development, critical considerations of AI in education is less visible. The paper highlights the necessity to steer the conceptualisation of AI literacy to put a stronger emphasis on critical orientations that enable students as well as teachers to examine claims about AI, and pose ethical questions to its adoption and use in classrooms and beyond.

As Artificial Intelligence-driven decision-making systems become increasingly popular, ensuring fairness in their outcomes has emerged as a critical and urgent challenge. AI models, often trained on open-source datasets embedded with human and systemic biases, risk producing decisions that disadvantage certain demographics. This challenge intensifies when multiple sensitive attributes interact, leading to intersectional bias, a compounded and uniquely complex form of unfairness. Over the years, various methods have been proposed to address bias at the data and model levels. However, mitigating intersectional bias in decision-making remains an under-explored challenge. Motivated by this gap, we propose a novel framework that leverages knowledge distillation to promote intersectional fairness. Our approach proceeds in two stages: first, a teacher model is trained solely to maximize predictive accuracy, followed by a student model that inherits the teacher's representational knowledge while incorporating intersectional fairness constraints. The student model integrates tailored loss functions that enforce parity in false positive rates and demographic distributions across intersectional groups, alongside an adversarial objective that minimizes protected attribute information within the learned representation. Empirical evaluation across multiple benchmark datasets demonstrates that we achieve a 52% increase in accuracy for multi-class classification and a 61% reduction in average false positive rate across intersectional groups and outperforms state-of-the-art models. This distillation-based methodology provides a more stable optimization opportunity than direct fairness approaches, resulting in substantially fairer representations, particularly for multiple sensitive attributes and underrepresented demographic intersections.

The generation of high-quality, long-sequenced time-series data is essential due to its wide range of applications. In the past, standalone Recurrent and Convolutional Neural Network based Generative Adversarial Networks (GAN) were used to synthesize time-series data. However, they are inadequate for generating long sequences of time-series data due to limitations in the architecture, such as difficulties in capturing long-range dependencies, limited temporal coherence, and scalability challenges. Furthermore, GANs are well known for their training instability and mode collapse problem. To address this, we propose TransFusion, a diffusion, and transformers-based generative model to generate high-quality long sequence time-series data. We extended the sequence length to 384, surpassing the previous limit, and successfully generated high quality synthetic data. Also, we introduce two evaluation metrics to evaluate the quality of the synthetic data as well as its predictive characteristics. TransFusion is evaluated using a diverse set of visual and empirical metrics, consistently outperforming the previous state-of-the-art by a significant margin.

Exploration in dynamic and uncertain real-world environments is an open problem in robotics and it constitutes a foundational capability of autonomous systems operating in most of the real-world. While 3D exploration planning has been extensively studied, the environments are assumed static or only reactive collision avoidance is carried out. We propose a novel approach to not only avoid dynamic obstacles but also include them in the plan itself, to deliberately exploit the dynamic environment in the agent's favor. The proposed planner, Dynamic AutonomousExploration Planner (DAEP), extends AEP [1] to explicitly plan with respect to dynamic obstacles. Furthermore, addressing prior errors within AEP in DAEP has resulted in enhanced exploration within static environments. To thoroughly evaluate exploration planners in dynamic settings, we propose a new enhanced benchmark suite with several dynamic environments, including large-scale outdoor environments. DAEP outperforms state-of-the-art planners in dynamic and large-scale environments and is shown to be more effective at both exploration and collision avoidance.

Exploration in dynamic and uncertain real-world environments is an open problem in robotics and it constitutes a foundational capability of autonomous systems operating in most of the real-world. While 3D exploration planning has been extensively studied, the environments are assumed static or only reactive collision avoidance is carried out. We propose a novel approach to not only avoid dynamic obstacles but also include them in the plan itself, to deliberately exploit the dynamic environment in the agent’s favor. The proposed planner, Dynamic Autonomous Exploration Planner (DAEP), extends AEP to explicitly plan with respect to dynamic obstacles. Furthermore, addressing prior errors within AEP in DAEP has resulted in enhanced exploration within static environments. To thoroughly evaluate exploration planners in such settings we propose a new enhanced benchmark suite with several dynamic environments, including large-scale outdoor environments. DAEP outperforms state-of-the-art planners in dynamic and large-scale environments and is shown to be more effective at both exploration and collision avoidance.