trust and safety engineering

This work addresses the challenge of ensuring safety, reliability, and trustworthiness in collective adaptive systems operating in dynamic environments by proposing a modular design paradigm centered on intrinsic trustworthiness. The approach integrates a runtime model based on local causal event sequences, a temporal logic verification technique supporting modular architectures, and a compositional reasoning mechanism for global system properties grounded in component attributes. Through this tripartite framework, the study overcomes key limitations of conventional formal methods and demonstrates substantial improvements in verifiability and scalability in case studies, thereby establishing both a theoretical foundation and a practical pathway for engineering highly trustworthy collective adaptive systems.

Online platforms face persistent challenges related to rule violations, harmful interactions, and the spread of misinformation. Method: This study proposes a “prosocial design” framework—grounded in behavioral science, human-computer interaction, and platform governance—that systematically articulates core design principles and clarifies their theoretical and practical relationship with trust and safety (T&S) systems. Unlike traditional reactive risk mitigation, the framework emphasizes proactive behavioral shaping through intentional interface and interaction design. Contribution/Results: Empirical validation demonstrates that prosocial design interventions significantly reduce rule violation rates and harmful information dissemination. The framework advances a novel, actionable, and evaluable paradigm for platform trust—one that shifts governance from passive enforcement to active norm cultivation—thereby providing both theoretical foundations and implementable tools for responsible platform design and policy.

How can users dynamically calibrate trust in automated systems—appropriately relying when the system is correct and promptly rejecting it when erroneous? This study proposes six interdisciplinary design principles, pioneering the systematic integration of pragmatics’ “common ground” theory and Grice’s cooperative principles into human–computer interaction (HCI) design, thereby establishing a dynamic, context-aware framework for credibility perception alignment. Methodologically, it synthesizes cognitive psychology, user experience (UX) design, and ethics, with emphasis on transparency and communicative effectiveness. Contributions include: (1) the first translation of foundational pragmatic theories into actionable, HCI-oriented design heuristics; (2) a structured, empirically grounded guideline supporting precise trust assessment; and (3) demonstrable improvements in human–AI collaboration safety, efficiency, and user satisfaction, alongside applicability to diagnostic evaluation of existing systems’ trustworthiness.

Prior security development research lacks empirical grounding, particularly regarding engineers’ practical challenges in industrially engineering and maintaining security features (e.g., encryption, access control). Method: We conducted a qualitative study involving semi-structured interviews with 26 experienced practitioners, followed by thematic coding to empirically validate and refine four prevalent industry assumptions. Contribution/Results: We identify three core challenges: (1) ambiguous security trade-off decisions, (2) severe documentation deficits, and (3) excessive maintenance burden during system evolution. We further characterize recurring code patterns and maintenance bottlenecks associated with security features. This work fills a critical gap in empirical security engineering research and provides actionable, evidence-based insights for designing security tools, IDE plugins, and engineering guidelines—thereby bridging the theory–practice divide in secure software development.

This study addresses the lack of systematic research and a unified framework concerning trust in digital twin systems. Through a systematic literature review and content mapping, complemented by a qualitative analytical framework, the work categorizes and synthesizes trust challenges and enhancement strategies reported in existing review literature. It identifies seven core trust challenges along with their corresponding mitigation strategies, proposes four types of trust integration models, and reveals four distinct trust-prioritization paradigms: human-centric, safety-critical, context-specific, and technology-driven. Innovatively, the study advocates for emerging directions such as trust-by-design, embedding trust metadata, and examining architectural impacts on trust, thereby establishing a theoretical foundation and research roadmap for developing trustworthy digital twin systems.

This study addresses the security risks arising from semantic mismatches in data that crosses trust boundaries, even when such data passes syntactic validation. It introduces the first systematic definition of the “Trust Boundary Semantic Gap” (TBSG) and proposes a Multidimensional Trust Boundary Semantic Gap (MDTBSG) model that characterizes TBSG along four dimensions: identity, space, time, and interpretation. Furthermore, the work develops the TBSAM framework for the design phase, integrating static specification analysis, semantic alignment modeling, gap provenance tracing, and architectural control mapping to identify, prioritize, and mitigate semantic gaps. Applied retrospectively to the SolarWinds/SUNBURST attack, the approach successfully pinpointed the root cause of critical semantic gaps, clarified assumptions in the receiving domain, and recommended effective architectural controls to disrupt the attack path.

This study addresses the lack of effective workflows and IDE tools that support end-to-end trust calibration for developers reviewing multi-file code changes generated by large language models (LLMs). In collaboration with JetBrains, the authors employed a double-diamond design process through participatory design to propose a three-tiered review workflow—comprising overview, file-level analysis, and code snippet inspection—centered on trust calibration, along with seven key design components. A high-fidelity, semi-interactive prototype was developed and evaluated, with results showing that the three-tiered workflow received significantly higher ratings than a neutral baseline. Notably, 63% of participants anticipated reduced review effort, and 52% reported a lower burden in assessing trustworthiness, demonstrating the framework’s effectiveness and potential for building AI-ready code review tools.

This work addresses the challenge of ensuring trustworthiness in the deployment of autonomous agents within critical engineering systems. It establishes trustworthiness as a core engineering attribute and proposes a unified assurance framework spanning the entire lifecycle—from perception to audit—structured around five key dimensions: safety constraints, robustness, transparency, accountability, and privacy preservation. The study innovatively formulates trustworthiness as a cross-domain commonality and introduces a reusable assurance paradigm analogous to the tiered certification approaches used in safety-critical systems. A systematic technical pathway is developed through integration of multidimensional trust models, architectural analysis, threat modeling, trust mechanisms, and quantitative evaluation metrics. The framework’s cross-domain applicability and effectiveness are validated through common design patterns and failure mode analyses in four representative domains: power systems, autonomous driving, high-performance computing, and communication networks.