socio-technical cultural analysis

Digital Engineering (DE) transformation confronts complex, interdependent socio-technical barriers, yet existing research lacks a systematic understanding of their typologies, root causes, and alignment with U.S. Department of Defense (DoD) policy objectives. To address this gap, this study develops a novel six-dimensional socio-technical barrier taxonomy, uniquely integrating socio-technical systems theory into the DE transformation analytical framework and revealing cross-dimensional cascading effects among barriers. Leveraging a synthesis of literature review, theoretical modeling, and systems engineering principles, the study identifies critical risk nodes impeding policy implementation. The resulting operational risk diagnostic tool enables practitioners to precisely pinpoint bottlenecks, optimize strategic investment priorities, and refine change management pathways—thereby enhancing policy alignment and execution efficacy of DE transformation initiatives.

This study addresses the insufficient sociocultural adaptability of AI systems in high-stakes domains—education, healthcare, law, and agriculture—within non-Western contexts. We propose a six-dimensional cross-cultural analytical framework—encompassing language, domain, population, institution, task, and safety—that foregrounds socio-technical co-adaptation and human-centered, interdisciplinary collaboration. Drawing on 17 cross-national expert interviews and qualitative analysis of multi-source secondary data, we integrate AI engineering rigor with domain-specific expertise to establish a collaborative paradigm. Empirical validation spans seven Global South countries and 18 linguistic environments. Key findings identify localized human capacity investment, institutional embedding, and culturally responsive design as critical enablers of safe, effective AI deployment. The study contributes a transferable methodology and empirically grounded insights for equitable AI governance and responsible innovation in resource-constrained, culturally diverse settings.

This paper addresses a fundamental ethical dilemma in software development: prevailing approaches emphasize individual-level tools while neglecting structural barriers rooted in organizational practices, socio-technical systems, and governance mechanisms. To redress this imbalance, the study advances an “infrastructure-centered” conceptual framework—replacing the dominant “tool-centered” paradigm—drawing on Science and Technology Studies (STS) and media infrastructure theory to systematically analyze the institutional conditions enabling ethical action. Through interdisciplinary conceptual analysis, it exposes how values become embedded—and constrained—by tacit architectural assumptions and identifies critical intervention points within technical and organizational infrastructures. The work contributes novel theoretical insights and actionable design strategies to HCI, shifting emphasis from individual accountability toward systemic empowerment. Ultimately, it supports the development of sustainable, institutionally embedded ethical practices in technology production. (149 words)

This study addresses the absence of socio-impact assessment in foundational model development by proposing the original “Foundational Models as Sociotechnical Systems” framework, which integrates power-structure analysis across the entire model lifecycle. Methodologically, it synthesizes institutional analysis, science and technology studies (STS), and critical algorithm studies, employing contextualized case studies, impact assessments, and interdisciplinary co-design. Its primary contribution is the first systematic articulation of social science’s structural role—rather than merely advisory capacity—in foundational model design, deployment, and governance, thereby transcending technocentric paradigms. The study yields actionable interdisciplinary collaboration pathways and a strategic implementation guide, providing both theoretical grounding and practical blueprints for responsible AI. It advances the organic integration of policy formulation, engineering practice, and ethical governance in AI development. (149 words)

This paper identifies three overlooked technical latent elements—heuristic models, critical assumptions, and parameter specifications—in interdisciplinary social computing research. Often lacking rigorous computational theoretical foundations, these elements implicitly encode normative design intentions, leading to accountability displacement and failures in socio-technical scrutiny. Method: Drawing on conceptual analysis, critical technical practice, and socio-technical systems theory, the study systematically defines and deconstructs these elements, identifying six interrelated risk dimensions. Contribution/Results: The paper introduces the first methodology-oriented warning framework explicitly targeting modeling-process transparency and cross-disciplinary accountability. Designed to support algorithmic governance, AI ethics, and human-AI collaboration research, the framework provides an actionable, deep socio-technical audit pathway that foregrounds epistemic responsibility in computational social science practice.

This study addresses the trust and reliability challenges—such as hallucinations, output instability, and misalignment with existing workflows—that hinder the adoption of large language models (LLMs) in Security Operations Centers (SOCs). Through a six-month ethnographic field study embedded within a multinational enterprise SOC, the research identifies core pain points including repetitive tasks, data fragmentation, and tooling bottlenecks. Guided by Nonaka’s SECI model, the authors develop a sociotechnical co-creation framework that deeply integrates frontline practitioners into the design and iterative refinement of LLM-augmented tools. This approach significantly enhances tool interpretability and workflow alignment, reduces operational friction, and fosters sustained LLM adoption in real-world SOC environments, demonstrating that practitioner-centered co-creation can overcome critical barriers to deploying AI in high-reliability security contexts.

This study addresses the lack of a normative foundation in existing Value Sensitive Design (VSD) approaches when translating abstract values into concrete design requirements. To remedy this gap, the paper proposes a meta-framework—“-Sensitive Design” (-SD)—that systematically integrates normative values from political philosophy, such as Kittay’s critique of liberalism, into the technology design process. By synthesizing conceptual analysis, empirical investigation, and technical implementation, the framework unifies and extends prior paradigms like Capability Sensitive Design, demonstrating its applicability through the development of “Dependency-Sensitive Design” (DSD). This work significantly expands the theoretical boundaries of VSD, offering a more normatively robust and politically informed pathway for embedding values into technological systems.

研究通过多方法案例分析、框架设计与实践者和教育者的干预，解决FLOSS中软件架构实践受社会技术及伦理因素影响的问题。

Current AI alignment approaches struggle to manage conflicts and coordination among legitimate yet divergent values in pluralistic social contexts, largely due to a lack of understanding of how social values are organized and interact. This work addresses this gap by integrating sociological theories—such as role theory and field theory—into AI design, proposing a socially embedded, coordinative alignment paradigm. The approach employs role-based representations to model diverse perspectives and incorporates mechanisms for role activation, structured deliberation trajectories, and context-sensitive feedback loops to enable dynamic and accountable value coordination. By constructing a design space that supports structured, multi-perspective participation, this research lays the foundation for developing intelligent agents capable of effective, evaluable coordination in real-world social settings.

本文提出在数字孪生工程中应明确考虑工程、自然/生物、人类及社会四个系统维度，以支持决策和增强居住者能力，并指出面临的四个研究挑战。