arXiv:2607. 11771v1 Announce Type: cross Abstract: Explainability has emerged as a critical requirement for AI-based systems, particularly in safety-critical and regulated domains.
arXiv:2610.14356v1 Announce Type: new Abstract: AI-enabled systems must remain dependable when the conditions under which they operate change (e.g., due to input perturbations, distribution shifts, o...
arXiv:2606. 16786v1 Announce Type: new Abstract: Algorithmic explanations are intended to help stakeholders understand opaque algorithmic decisions, but in practice, they often fall short.
arXiv:2607. 04448v1 Announce Type: cross Abstract: Ensuring software compliance with regulations such as the General Data Protection Regulation (GDPR) and the Artificial Intelligence Act (EU AI Act) poses a significant challenge, as requirements engineers must translate complex legal text into actionable software requirements - a process that remains largely manual and error-prone in practice.
The study examines how practitioners in AI-driven systems define, assess, and manage data quality, revealing six key themes. It highlights shifts in traceability, the use of models as quality assessors, and the emergence of new data objects such as agent context and synthetic data. The research proposes a lifecycle assurance framework to provide evidence that data supports specific AI claims throughout model behavior, judgments, and agent actions.
arXiv:2502. 00837v3 Announce Type: replace-cross Abstract: Natural Language Processing (NLP) is now embedded in critical sectors including healthcare, finance, and customer relationship management, where models such as GPT-4o, Gemini, and BERT increasingly inform decisions.
arXiv:2606. 09568v1 Announce Type: new Abstract: The growing complexity of self-adaptive and self-organising systems, fuelled by advances in Artificial Intelligence (AI), has made them increasingly difficult to understand and trust.
arXiv:2505. 10300v2 Announce Type: replace-cross Abstract: Responsible AI (RAI) efforts increasingly emphasize the importance of addressing potential harms early in the AI development lifecycle through social-technical lenses.
arXiv:2608.30956v1 Announce Type: cross Abstract: Counterfactual explanations (CEs) are widely used in explainable artificial intelligence (AI) to show how a model's outputs would change if the input...
arXiv:2606. 14306v1 Announce Type: cross Abstract: Current Generative AI (GenAI) interfaces remain largely constrained to chatbox interaction, which can impose high cognitive demands on users and create substantial barriers for people with intellectual disabilities (ID), including prompt formulation difficulties, response overload, and limited mechanisms to assess information reliability.
The paper introduces Constraint-Driven Context Engineering (CDCE), a design approach that treats domain constraints as primary drivers for creating AI system interfaces. CDCE identifies, characterises, and operationalises constraints to determine necessary context assets and their representations, improving the quality and domain appropriateness of AI-generated solutions. A comparative multiple‑case study across education, healthcare, and finance demonstrates CDCE’s applicability and shows how constraint characteristics shape the resulting interfaces.
arXiv:2607. 14123v1 Announce Type: cross Abstract: Despite the proliferation of Explainable AI (XAI) techniques -- from feature attributions to sparse autoencoders -- explanations rarely influence real-world workflows.
arXiv:2606. 31589v1 Announce Type: cross Abstract: Organisations designing, developing, and deploying machine learning systems (MLS) need to be able to check that these systems are trustworthy, and communicate this clearly to their stakeholders, be they different categories of users, engineers, or wider society.