socio-technical contextualization

This study addresses the question of how to systematically uncover the political ideologies embedded within technological projects. By integrating critical and emancipatory traditions in the social sciences, it proposes a dialectical analytical framework that juxtaposes value orientations with structural constraints, thereby transcending the limitations of one-dimensional ideological interpretations. The framework synthesizes critical social theory with methods of ideological analysis to yield an operationalizable paradigm, whose explanatory power is demonstrated through empirical case studies. The primary contribution of this research lies in offering a novel approach that combines theoretical rigor with practical feasibility, enabling a systematic deconstruction of the political dimensions inherent in technological endeavors.

This study examines second-order effects—such as drift and ossification—that emerge over long-term protocol evolution (i.e., rules, standards, and coordination mechanisms), revealing how political transformations arise during cross-community and intergenerational transmission due to ambiguous handovers, adversarial reinterpretation, cultural shifts, and crisis-driven adaptation. Method: We propose “Protocol Futuring,” an analytical framework that treats protocols as speculative design artifacts. It employs relay-style multi-team workshops, scenario-based simulation, and successor-oriented collaborative architecture to surface infrastructure’s latent politics and long-term socio-technical consequences. Contribution/Results: Validated through the Knowledge Futurama case—a millennium-scale knowledge preservation initiative—the framework demonstrates explanatory power and intervention potential for understanding and shaping the long-term evolution of sociotechnical systems, particularly where institutional continuity, epistemic authority, and infrastructural endurance intersect.

Current conceptions of technical excellence in AI prioritize performance and short-term innovation while neglecting ethical, social, and cultural dimensions, rendering them inadequate for addressing the complex challenges posed by generative and embodied artificial intelligence. This work proposes a reconceptualization of “technical excellence” as an integrated framework encompassing ethical robustness, societal intelligibility, and long-term relevance. Through interdisciplinary collaboration, it embeds insights from the humanities and social sciences throughout the entire AI development lifecycle. Focusing on agenda-setting, foresight, education, communication, and institutional design, the project employs methods including ethical analysis, socio-technical forecasting, pedagogical design, visual communication, and institutional innovation to foster structural integration. The resulting approach offers a pathway for cutting-edge AI advancement that harmonizes technical rigor with social responsibility, thereby advancing responsible innovation and sustainable governance.

Contemporary information retrieval (IR) research predominantly adopts a reactive, risk-avoidance stance toward societal harms, neglecting proactive critical reflection on and constructive articulation of the sociotechnical imaginaries embedded in IR systems. Method: This paper advances a paradigm shift—centering explicit, pluralistic sociotechnical imaginaries—and systematically integrates democratic theory, critical theory, and social justice praxis to formulate a new agenda for socially just information access. Drawing on interdisciplinary perspectives from science and technology studies (STS), human–computer interaction, media studies, and critical information studies, it moves beyond algorithm-centric approaches toward institutional critique and collaborative action. Contribution/Results: The paper introduces, for the first time in IR, an operational “theory of change” framework—a systematic guide enabling the field to reorient its research agenda, technical visions, and modes of collaboration—from harm mitigation toward values-driven design practice.

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 critiques the prevailing paradigm that equates design with problem-solving, which often reduces complex socio-political issues to tractable technical challenges—a tendency emblematic of “technological solutionism” that overlooks the inherently political and value-laden nature of problem formulation. To address this, the project introduces the WPR (What’s the Problem Represented to be?) analytical framework from policy studies into design and technology research for the first time. Integrating insights from philosophy of technology and design theory, it offers a dual discursive-material critique of technological artifacts. This approach systematically uncovers the problem representations and ideological presuppositions embedded within technical objects, providing a practical reflective tool that not only challenges technological solutionism but also opens new interdisciplinary pathways between design theory and the philosophy of technology.

This study examines how generative models commodify and privatize human sociality, thereby reshaping social relations. Introducing the concept of “social practices,” the work distinguishes between use-value and exchange-value dimensions of sociality, revealing how generative models substitute for and mediate social interaction. It pioneers a theoretical framework of “synthetic sociality,” extending critical analysis beyond cognitive labor to encompass the social dimension. Drawing on critical theory, historical political economy, and interdisciplinary empirical methods, the research systematically elucidates the structural role of generative models within digital capitalism. It critiques the undemocratic governance of these systems by private technology firms and provides a normative foundation for democratized, accountable AI design.

This study addresses how artificial intelligence can be effectively integrated into organizational practices in alignment with the principles of Human-Centered AI (HCAI), rather than being treated merely as a technical tool. Drawing on sociotechnical systems theory and HCAI principles, the research develops a multidimensional integration framework through an analysis of ten predictive maintenance cases, revealing how AI becomes embedded within organizational communication, collaboration, and decision-making processes. The findings demonstrate that AI implementation fosters cross-disciplinary interpretation of outputs, enables dynamic workflow adjustments, and supports iterative refinement of operational rules. This, in turn, drives organizational learning, enhances quality assurance, and facilitates continuous improvement, ultimately cultivating a novel mode of human-AI collaboration that reconfigures work practices around shared intelligence and mutual adaptation.

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.

This work addresses a critical gap in current responsible innovation and AI ethics frameworks, which largely overlook the agency of technology practitioners within geopolitical contexts and the narrative structures in which they are embedded. The project proposes an AI-driven interactive storytelling system that integrates speculative scenarios, archetypal character modeling, and collaborative reflection workshops to guide technologists—through non-didactic means—in critically examining the implicit values, assumptions, and positionalities embedded in their technical practices. By uniquely merging speculative narrative with geopolitical reflection, this approach transcends conventional moral prescriptivism, offering practitioners a novel methodological tool to explore the entanglements of technology, power, and geopolitics, thereby fostering a heightened critical awareness of the geopolitical dimensions of their work.