One major challenge for the adoption and acceptance of automated vehicles (AVs) is ensuring that they can make sound decisions in everyday situations that involve ethical tension. Much attention has focused on rare, high-stakes dilemmas such as trolley problems. Yet similar conflicts arise in routine driving when human considerations, such as legality, efficiency, and comfort, come into conflict. Current AV planning systems typically rely on rigid rules, which struggle to balance these competing considerations and often lead to behaviour that misaligns with human expectations. This paper introduces a reasons-based trajectory evaluation framework that operationalises the tracking condition of Meaningful Human Control (MHC). The framework represents human agents reasons (e.g., regulatory compliance) as quantifiable functions and evaluates how well candidate trajectories align with them. It assigns adjustable weights to agent priorities and includes a balance function to discourage excluding any agent. To demonstrate the approach, we use a real-world-inspired overtaking scenario, which highlights tensions between compliance, efficiency, and comfort. Our results show that different trajectories emerge as preferable depending on how agents reasons are weighted, and small shifts in priorities can lead to discrete changes in the selected action. This demonstrates that everyday ethical decisions in AV driving are highly sensitive to the weights assigned to the reasons of different human agents.

Retrieval-Augmented Generation (RAG) shows significant promise in knowledge-intensive tasks by improving domain specificity, enhancing temporal relevance, and reducing hallucinations. However, applying RAG to finance encounters critical challenges: restricted access to proprietary datasets, limited retrieval accuracy, regulatory constraints, and sensitive data interpretation. We introduce AstuteRAG-FQA, an adaptive RAG framework tailored for Financial Question Answering (FQA), leveraging task-aware prompt engineering to address these challenges. The framework uses a hybrid retrieval strategy integrating both open-source and proprietary financial data while maintaining strict security protocols and regulatory compliance. A dynamic prompt framework adapts in real time to query complexity, improving precision and contextual relevance. To systematically address diverse financial queries, we propose a four-tier task classification: explicit factual, implicit factual, interpretable rationale, and hidden rationale involving implicit causal reasoning. For each category, we identify key challenges, datasets, and optimization techniques within the retrieval and generation process. The framework incorporates multi-layered security mechanisms including differential privacy, data anonymization, and role-based access controls to protect sensitive financial information. Additionally, AstuteRAG-FQA implements real-time compliance monitoring through automated regulatory validation systems that verify responses against industry standards and legal obligations. We evaluate three data integration techniques - contextual embedding, small model augmentation, and targeted fine-tuning - analyzing their efficiency and feasibility across varied financial environments.

Medical AI faces challenges in privacy-preserving collaborative learning while ensuring fairness across heterogeneous healthcare institutions. Current federated learning approaches suffer from static architectures, slow convergence (45-73 rounds), fairness gaps marginalizing smaller institutions, and scalability constraints (15-client limit). We propose Adaptive Fair Federated Learning (AFFL) through three innovations: (1) Adaptive Knowledge Messengers dynamically scaling capacity based on heterogeneity and task complexity, (2) Fairness-Aware Distillation using influence-weighted aggregation, and (3) Curriculum-Guided Acceleration reducing rounds by 60-70%. Our theoretical analysis provides convergence guarantees with epsilon-fairness bounds, achieving O(T^{-1/2}) + O(H_max/T^{3/4}) rates. Projected results show 55-75% communication reduction, 56-68% fairness improvement, 34-46% energy savings, and 100+ institution support. The framework enables multi-modal integration across imaging, genomics, EHR, and sensor data while maintaining HIPAA/GDPR compliance. We propose MedFedBench benchmark suite for standardized evaluation across six healthcare dimensions: convergence efficiency, institutional fairness, privacy preservation, multi-modal integration, scalability, and clinical deployment readiness. Economic projections indicate 400-800% ROI for rural hospitals and 15-25% performance gains for academic centers. This work presents a seven-question research agenda, 24-month implementation roadmap, and pathways toward democratizing healthcare AI.

Artificial Intelligence has rapidly become a cornerstone technology, significantly influencing Europe's societal and economic landscapes. However, the proliferation of AI also raises critical ethical, legal, and regulatory challenges. The CERTAIN (Certification for Ethical and Regulatory Transparency in Artificial Intelligence) project addresses these issues by developing a comprehensive framework that integrates regulatory compliance, ethical standards, and transparency into AI systems. In this position paper, we outline the methodological steps for building the core components of this framework. Specifically, we present: (i) semantic Machine Learning Operations (MLOps) for structured AI lifecycle management, (ii) ontology-driven data lineage tracking to ensure traceability and accountability, and (iii) regulatory operations (RegOps) workflows to operationalize compliance requirements. By implementing and validating its solutions across diverse pilots, CERTAIN aims to advance regulatory compliance and to promote responsible AI innovation aligned with European standards.

Generating regulatorily compliant Suspicious Activity Report (SAR) remains a high-cost, low-scalability bottleneck in Anti-Money Laundering (AML) workflows. While large language models (LLMs) offer promising fluency, they suffer from factual hallucination, limited crime typology alignment, and poor explainability -- posing unacceptable risks in compliance-critical domains. This paper introduces Co-Investigator AI, an agentic framework optimized to produce Suspicious Activity Reports (SARs) significantly faster and with greater accuracy than traditional methods. Drawing inspiration from recent advances in autonomous agent architectures, such as the AI Co-Scientist, our approach integrates specialized agents for planning, crime type detection, external intelligence gathering, and compliance validation. The system features dynamic memory management, an AI-Privacy Guard layer for sensitive data handling, and a real-time validation agent employing the Agent-as-a-Judge paradigm to ensure continuous narrative quality assurance. Human investigators remain firmly in the loop, empowered to review and refine drafts in a collaborative workflow that blends AI efficiency with domain expertise. We demonstrate the versatility of Co-Investigator AI across a range of complex financial crime scenarios, highlighting its ability to streamline SAR drafting, align narratives with regulatory expectations, and enable compliance teams to focus on higher-order analytical work. This approach marks the beginning of a new era in compliance reporting -- bringing the transformative benefits of AI agents to the core of regulatory processes and paving the way for scalable, reliable, and transparent SAR generation.

In today's business landscape, organizations need to find the right balance between using their customers' data ethically to power AI solutions and being compliant regarding data privacy and data usage regulations. In this paper, we discuss synthetic data as a possible solution to this dilemma. Synthetic data is simulated data that mimics the real data. We explore how organizations in heavily regulated industries, such as financial institutions or healthcare organizations, can leverage synthetic data to build robust AI solutions while staying compliant. We demonstrate that synthetic data offers two significant advantages by allowing AI models to learn from more diverse data and by helping organizations stay compliant against data privacy laws with the use of synthetic data instead of customer information. We discuss case studies to show how synthetic data can be effectively used in the finance and healthcare sector while discussing the challenges of using synthetic data and some ethical questions it raises. Our research finds that synthetic data could be a game-changer for AI in regulated industries. The potential can be realized when industry, academia, and regulators collaborate to build solutions. We aim to initiate discussions on the use of synthetic data to build ethical, responsible, and effective AI systems in regulated enterprise industries.

The recent tremendous advancements in the areas of Artificial Intelligence (AI) and Deep Learning (DL) have also resulted into corresponding remarkable progress in the field of Computer Vision (CV), showcasing robust technological solutions in a wide range of application sectors of high industrial interest (e.g., healthcare, autonomous driving, automation, etc.). Despite the outstanding performance of CV systems in specific domains, their development and exploitation at industrial-scale necessitates, among other, the addressing of requirements related to the reliability, transparency, trustworthiness, security, safety, and robustness of the developed AI models. The latter raises the imperative need for the development of efficient, comprehensive and widely-adopted industrial standards. In this context, this study investigates the current state of play regarding the development of industrial computer vision AI standards, emphasizing on critical aspects, like model interpretability, data quality, and regulatory compliance. In particular, a systematic analysis of launched and currently developing CV standards, proposed by the main international standardization bodies (e.g. ISO/IEC, IEEE, DIN, etc.) is performed. The latter is complemented by a comprehensive discussion on the current challenges and future directions observed in this regularization endeavor.

Existing legal frameworks on AI rely on training compute thresholds as a proxy to identify potentially-dangerous AI models and trigger increased regulatory attention. In the United States, Section 4.2(a) of Executive Order 14110 instructs the Secretary of Commerce to require extensive reporting from developers of AI models above a certain training compute threshold. In the European Union, Article 51 of the AI Act establishes a presumption that AI models above a certain compute threshold have high impact capabilities and hence pose systemic risk, thus subjecting their developers to several obligations including capability evaluations, reporting, and incident monitoring. In this paper, we examine some enhancement techniques that are capable of decreasing training compute usage while preserving, or even increasing, model capabilities. Since training compute thresholds rely on training compute as a metric and trigger for increased regulatory attention, these capability-enhancing and compute-saving techniques could constitute a legal loophole to existing training compute thresholds. In particular, we concentrate on four illustrative techniques (fine-tuning, model reuse, model expansion, and above compute-optimal inference compute) with the goal of furthering the conversation about their implications on training compute thresholds as a legal mechanism and advancing policy recommendations that could address the relevant legal loopholes.

The many initiatives on trustworthy AI result in a confusing and multipolar landscape that organizations operating within the fluid and complex international value chains must navigate in pursuing trustworthy AI. The EU's AI Act will now shift the focus of such organizations toward conformance with the technical requirements for regulatory compliance, for which the Act relies on Harmonized Standards. Though a high-level mapping to the Act's requirements will be part of such harmonization, determining the degree to which standards conformity delivers regulatory compliance with the AI Act remains a complex challenge. Variance and gaps in the definitions of concepts and how they are used in requirements between the Act and harmonized standards may impact the consistency of compliance claims across organizations, sectors, and applications. This may present regulatory uncertainty, especially for SMEs and public sector bodies relying on standards conformance rather than proprietary equivalents for developing and deploying compliant high-risk AI systems. To address this challenge, this paper offers a simple and repeatable mechanism for mapping the terms and requirements relevant to normative statements in regulations and standards, e.g., AI Act and ISO management system standards, texts into open knowledge graphs. This representation is used to assess the adequacy of standards conformance to regulatory compliance and thereby provide a basis for identifying areas where further technical consensus development in trustworthy AI value chains is required to achieve regulatory compliance.

Artificial Intelligence (AI) has made its way into various scientific fields, providing astonishing improvements over existing algorithms for a wide variety of tasks. In recent years, there have been severe concerns over the trustworthiness of AI technologies. The scientific community has focused on the development of trustworthy AI algorithms. However, machine and deep learning algorithms, popular in the AI community today, depend heavily on the data used during their development. These learning algorithms identify patterns in the data, learning the behavioral objective. Any flaws in the data have the potential to translate directly into algorithms. In this study, we discuss the importance of Responsible Machine Learning Datasets and propose a framework to evaluate the datasets through a responsible rubric. While existing work focuses on the post-hoc evaluation of algorithms for their trustworthiness, we provide a framework that considers the data component separately to understand its role in the algorithm. We discuss responsible datasets through the lens of fairness, privacy, and regulatory compliance and provide recommendations for constructing future datasets. After surveying over 100 datasets, we use 60 datasets for analysis and demonstrate that none of these datasets is immune to issues of fairness, privacy preservation, and regulatory compliance. We provide modifications to the ``datasheets for datasets" with important additions for improved dataset documentation. With governments around the world regularizing data protection laws, the method for the creation of datasets in the scientific community requires revision. We believe this study is timely and relevant in today's era of AI.