Abstract Autonomous agents are increasingly deployed in sensitive, human-centric domains—such as healthcare, assistive care, and emergency response—where their decision-making must align with complex human norms. These translate into Social, Legal, Ethical, Empathetic, and Cultural (SLEEC) requirements that are often nuanced and context-dependent, challenging traditional software engineering paradigms. Our tutorial paper presents a comprehensive, tool-supported methodology for managing the SLEEC requirements lifecycle, covering elicitation, well-formedness validation, and conformance verification of software design models against SLEEC requirements. We demonstrate the use of our methodology and associated tools through application to a robot-assisted dressing system, providing a guide for researchers and engineers to bridge the gap between abstract human norms and verifiable system designs.

Existing classification frameworks for AI and autonomous systems are being outpaced by recent advancements in AI technologies. This limits their applicability to modern intelligent systems, particularly agentic AI systems (autonomous systems that leverage foundation models to achieve wide-ranging, multi-layered goals). To address this deficiency, we introduce INSYTE, a multi-faceted framework that supports the classification of AI systems ranging from traditional rule-based systems to cutting-edge embodied AI and agentic systems. To that end, INSYTE considers the essential characteristics of an AI system across eight key dimensions grouped into four categories: system design ( underspecification and adaptiveness ); functionality ( breadth and depth ); operating environment ( diversity and dynamism ); and independence from human operational control ( intervention and oversight ). Different AI systems (or versions of systems) yield different ‘patterns’ on an eight-axis radar chart that INSYTE uses to provide an immediate visual summary of an AI system’s overall capability and a detailed representation of its individual characteristics. The INSYTE framework aligns with OECD’s definition of deployed AI systems, which is becoming the standard definition used by legislators and developers worldwide.

As software systems increasingly interact with humans in application domains such as transportation and healthcare, they raise concerns related to the social, legal, ethical, empathetic, and cultural (SLEEC) norms and values of their stakeholders. Normative non-functional requirements (N-NFRs) are used to capture these concerns by setting SLEEC-relevant boundaries for system behavior. Since N-NFRs need to be specified by multiple stakeholders with widely different, non-technical expertise (ethicists, lawyers, regulators, end users, etc.), N-NFR elicitation is very challenging. To address this difficult task, we introduce N-Check, a novel tool-supported formal approach to N-NFR analysis and debugging. N-Check employs satisfiability checking to identify a broad spectrum of N-NFR well-formedness issues, such as conflicts, redundancy, restrictiveness, and insufficiency, yielding diagnostics that pinpoint their causes in a user-friendly way that enables non-technical stakeholders to understand and fix them. We show the effectiveness and usability of our approach through nine case studies in which teams of ethicists, lawyers, philosophers, psychologists, safety analysts, and engineers used N-Check to analyse and debug 233 N-NFRs, comprising 62 issues for the software underpinning the operation of systems, such as, assistive-care robots and tree-disease detection drones to manufacturing collaborative robots.

Normative non-functional requirements specify con-straints that a system must observe in order to avoid violations of social, legal, ethical, empathetic, and cultural norms. As these requirements are typically defined by non-technical system stakeholders with different expertise and priorities (ethicists, lawyers, social scientists, etc.), ensuring their well-formedness and consistency is very challenging. Recent research has tackled this challenge using a domain-specific language to specify normative requirements as rules whose consistency can then be analysed with formal methods. In this paper, we propose a complemen-tary approach that uses Large Language Models to extract semantic relationships between abstract representations of system capabilities. These relations, which are often assumed implicitly by non-technical stakeholders (e.g., based on common sense or domain knowledge), are then used to enrich the automated reasoning techniques for eliciting and analyzing the consistency of normative requirements. We show the effectiveness of our approach to normative requirements elicitation and operational-ization through a range of real-world case studies. An extended version of this paper, which includes appendices is available at https://arxiv.org/abs/2404.12335