The rise of Artificial Intelligence (AI) is reshaping how software systems are developed and maintained. However, AI-based systems give rise to new software issues that existing detection tools often miss. Among these, we focus on AI-specific code smells, recurring patterns in the code that may indicate deeper problems such as unreproducibility, silent failures, or poor model generalization. We introduce SpecDetect4AI, a tool-based approach for the specification and detection of these code smells at scale. This approach combines a high-level declarative Domain-Specific Language (DSL) for rule specification with an extensible static analysis tool that interprets and detects these rules for AI-based systems. We specified 22 AI-specific code smells and evaluated SpecDetect4AI on 826 AI-based systems (20M lines of code), achieving a precision of 88.66% and a recall of 88.89%, outperforming other existing detection tools. Our results show that SpecDetect4AI supports the specification and detection of AI-specific code smells through dedicated rules and can effectively analyze large AI-based systems, demonstrating both efficiency and extensibility (SUS 81.7/100).

Abstract-- The interaction between humans and AI in safety-critical systems presents a unique set of challenges that re main partially addressed by existing frameworks. These challen ges stem from the complex interplay of requirements for transparency, trust, and explainability, coupled with the neces sity for robust and safe decision-making. A framework that holistic ally integrates human and AI capabilities while addressing thes e concerns is notably required, bridging the critical gaps in designing, deploying, and maintaining safe and effective sys tems. This paper proposes a holistic conceptual framework for cri tical infrastructures by adopting an interdisciplinary approac h. It integrates traditionally distinct fields such as mathemati cs, decision theory, computer science, philosophy, psycholog y, and cognitive engineering and draws on specialized engineerin g domains, particularly energy, mobility, and aeronautics. Its flexibility is further demonstrated through a case study on power grid management. Artificial Intelligence (AI) is showing high potential to transform the management of critical infrastructures [1], tackling pressing challenges like climate change and the rising demand for energy and mobility systems while advancing strategic objectives such as energy transition and digi tal transformation. On the other hand, integrating AI in critic al sectors introduces significant challenges, many of which ar e already being addressed by emerging regulatory frameworks, such as the European Union AI Act. These frameworks emphasize the importance of safety, transparency, and adhe r-ence to ethical standards and principles to mitigate a wide range of risks, including technical, social, and environme ntal hazards associated with deploying AI in high-risk domains. Another key challenge lies in fostering effective human-AI collaboration.

Japan's infrastructure ministry is developing a system that combines administrative information held by local governments to detect vacant houses using artificial intelligence. Based on information such as water usage, basic resident registers and real estate registries, the system uses AI to calculate the probability that a building is unoccupied. For example, if the building is an old wooden house with very low water use and only one elderly resident registered, the system displays a high probability that the house is unoccupied. As some vacant houses are difficult to identify from the exterior alone, the aim of the new system is to detect them at an early stage and make them available for sale or rent, or demolish them before they collapse.

Recent advancements in artificial intelligence (AI) applications within aerospace have demonstrated substantial growth, particularly in the context of control systems. As High Performance Computing (HPC) platforms continue to evolve, they are expected to replace current flight control or engine control computers, enabling increased computational capabilities. This shift will allow real-time AI applications, such as image processing and defect detection, to be seamlessly integrated into monitoring systems, providing real-time awareness and enhanced fault detection and accommodation. Furthermore, AI's potential in aerospace extends to control systems, where its application can range from full autonomy to enhancing human control through assistive features. AI, particularly deep reinforcement learning (DRL), can offer significant improvements in control systems, whether for autonomous operation or as an augmentative tool.

Artificial Intelligence (AI) has emerged as a key technology, driving advancements across a range of applications. Its integration into modern autonomous systems requires assuring safety. However, the challenge of assuring safety in systems that incorporate AI components is substantial. The lack of concrete specifications, and also the complexity of both the operational environment and the system itself, leads to various aspects of uncertain behavior and complicates the derivation of convincing evidence for system safety. Nonetheless, scholars proposed to thoroughly analyze and mitigate AI-specific insufficiencies, so-called AI safety concerns, which yields essential evidence supporting a convincing assurance case. In this paper, we build upon this idea and propose the so-called Landscape of AI Safety Concerns, a novel methodology designed to support the creation of safety assurance cases for AI-based systems by systematically demonstrating the absence of AI safety concerns. The methodology's application is illustrated through a case study involving a driverless regional train, demonstrating its practicality and effectiveness.

First and foremost, do no harm: algorithms that put a subset of the population at risk should not be deployed. This requires regulatory intervention, algorithmic research (for example, developing better algorithms for handling outliers)9 and applications research (for example, studying the risks that applications might create for disabled people). We must consider "the context in which such technology is produced and situated, the politics of classification, and the ways in which fluid identities are (mis)reflected and calcified through such technology."11 The most important step in avoiding this potential harm is to change who builds, regulates, and deploys AI-based systems. We must ensure disabled people contribute their perspective and expertise to the design of AI-based systems.

Using Large Language Models (LLMs) to address critical societal problems requires adopting this novel technology into socio-technical systems. However, the complexity of such systems and the nature of LLMs challenge such a vision. It is unlikely that the solution to such challenges will come from the Artificial Intelligence (AI) community itself. Instead, the Systems Engineering approach is better equipped to facilitate the adoption of LLMs by prioritising the problems and their context before any other aspects. This paper introduces the challenges LLMs generate and surveys systems research efforts for engineering AI-based systems. We reveal how the systems engineering principles have supported addressing similar issues to the ones LLMs pose and discuss our findings to provide future directions for adopting LLMs.

Ethical AI development is crucial as new technologies and concerns emerge, but objective, practical ethical guidance remains debated. This study examines LLMs in developing ethical AI systems, assessing how trustworthiness-enhancing techniques affect ethical AI output generation. Using the Design Science Research (DSR) method, we identify techniques for LLM trustworthiness: multi-agents, distinct roles, structured communication, and multiple rounds of debate. We design the multi-agent prototype LLM-BMAS, where agents engage in structured discussions on real-world ethical AI issues from the AI Incident Database. The prototype's performance is evaluated through thematic analysis, hierarchical clustering, ablation studies, and source code execution. Our system generates around 2,000 lines per run, compared to only 80 lines in the ablation study. Discussions reveal terms like bias detection, transparency, accountability, user consent, GDPR compliance, fairness evaluation, and EU AI Act compliance, showing LLM-BMAS's ability to generate thorough source code and documentation addressing often-overlooked ethical AI issues. However, practical challenges in source code integration and dependency management may limit smooth system adoption by practitioners. This study aims to shed light on enhancing trustworthiness in LLMs to support practitioners in developing ethical AI-based systems.

Artificial intelligence (AI) permeates all fields of life, which resulted in new challenges in requirements engineering for artificial intelligence (RE4AI), e.g., the difficulty in specifying and validating requirements for AI or considering new quality requirements due to emerging ethical implications. It is currently unclear if existing RE methods are sufficient or if new ones are needed to address these challenges. Therefore, our goal is to provide a comprehensive overview of RE4AI to researchers and practitioners. What has been achieved so far, i.e., what practices are available, and what research gaps and challenges still need to be addressed? To achieve this, we conducted a systematic mapping study combining query string search and extensive snowballing. The extracted data was aggregated, and results were synthesized using thematic analysis. Our selection process led to the inclusion of 126 primary studies. Existing RE4AI research focuses mainly on requirements analysis and elicitation, with most practices applied in these areas. Furthermore, we identified requirements specification, explainability, and the gap between machine learning engineers and end-users as the most prevalent challenges, along with a few others. Additionally, we proposed seven potential research directions to address these challenges. Practitioners can use our results to identify and select suitable RE methods for working on their AI-based systems, while researchers can build on the identified gaps and research directions to push the field forward.

Software systems are inherently complex. In addition, any ML model is, at its core, probabilistic in nature and hence, suffers from the challenge of uncertainty [2, 3, 4]. The complexity of a software system combined with the non-deterministic nature of an ML model can introduce variability - the phenomenon where a piece of software behaves differently when the development or the runtime environment changes although the internal software artifacts such as code, and input data are exactly the same. In practice it is very likely that development and deployment environments are different, hence, understanding how an ML model may behave differently after deployment compared to how it behaved in the development environment is a crucial aspect of AI-based software development. For example, an arbitrary face recognition system achieving an F1-score of, say 0.9, in the development environment does not guarantee that it will on average achieve a similar F1-score once deployed in a different environment configuration.