socio-technical analysis

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 a critical gap in AI alignment transparency research, which has predominantly focused on informational aspects while overlooking how institutional and organizational forces shape alignment decisions and their societal implications. The paper introduces a novel “structural transparency” framework, integrating institutional logics theory into AI alignment for the first time. It develops a macro-level analytical system encompassing institutional logic identification, analysis of external perturbations, and mapping of structural risks. Operationalized through a taxonomy and a five-component analytical model—implemented via an “analyst recipe”—the framework offers a practical toolkit that meaningfully complements existing information-centric transparency approaches. This enables systematic assessment of institutional dynamics and sociotechnical risks inherent in AI alignment governance.

This study reconceptualizes AI alignment as a situated, ongoing, and co-constructed practice between humans and models, shifting focus from model-centric approaches to the active role of users in recognizing and responding to value misalignments during interaction. Through participatory workshops, misalignment diaries, and generative design activities, the research investigates how users understand and engage in alignment processes when using large language models as research assistants. Findings reveal that value misalignments often manifest as unexpected responses or disruptions in task execution and social interaction, prompting users to develop diverse strategies—ranging from prompt refinement and model behavior interpretation to deliberate disengagement. By foregrounding users as agentic cognitive participants, this work challenges dominant model-centered paradigms and offers a novel pathway toward human-centered alignment mechanisms.

Current AI alignment research predominantly focuses on single-agent systems or aggregate human preferences, neglecting systemic misalignment arising from heterogeneous objectives and conflicting preferences among multiple stakeholders. Method: This paper proposes a computational social science–driven multi-agent alignment framework—the first to integrate formal dispute modeling into AI alignment—by constructing weighted preference graphs and agent-based simulation models that enable cross-domain, multi-stakeholder, preference-weighted misalignment quantification. Contribution/Results: The approach transcends traditional unidimensional value alignment paradigms. Empirical evaluation in autonomous driving demonstrates its capacity to identify high-conflict stakeholder preference hotspots and reproduce canonical misalignment patterns. It significantly enhances interpretability of alignment failures and provides actionable guidance for socio-technical system design, thereby addressing a critical gap in modeling dynamic misalignment within complex, adaptive sociotechnical systems.

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 study addresses interpretive discrepancies between monitored individuals and supervising authorities in electronic monitoring systems, where divergent standpoints lead to misjudgments of behavior and imbalanced interactions. Drawing on China’s community correction system, the research employs semi-structured interviews (with 26 supervisees and 12 supervisors), situational analysis, and a CSCW theoretical framework to uncover structural misalignments in data interpretation. Introducing the concept of “interpretive misalignment,” the work reconceptualizes continuous sensing as distributed interpretive labor and identifies five categories of behavioral responses stemming from asymmetries in data, context, and inference. Building on these findings, the study proposes design directions that enhance transparency and mutual negotiability in data-driven decision-making, offering novel perspectives on intelligibility, contestability, and accountability across system boundaries.

This study addresses the fragmentation in AI alignment research stemming from competing conceptual frameworks, which can lead interventions to have opposing effects under different alignment perspectives. By systematically analyzing three dominant alignment paradigms, the work reveals that their fundamental disagreements arise from divergent threat models and normative orientations. Through conceptual analysis, comparison of research programs, and clarification of policy–science distinctions, the paper articulates— for the first time—the internal pluralism and tensions within alignment discourse. It proposes five recommendations to improve research practices and makes a key contribution by developing a refined conceptual framework that distinguishes idealized alignment goals from empirical proxy metrics. This framework provides a clearer terminological foundation and methodological guidance for interdisciplinary communication and technical intervention in AI alignment.

This study investigates how interface paradigms of data cleaning tools shape users’ actual cleaning strategies. Through a between-subjects observational experiment with 40 participants, it compares usage behaviors across Jupyter, Excel, ChatGPT, and OpenRefine on representative data cleaning tasks, applying for the first time the technical dimensions framework from programming systems to this domain. Findings reveal that interface design significantly steers user strategies without determining outcomes: data-centric interfaces (e.g., Excel) encourage opportunistic, ad-hoc operations, whereas abstraction-centric tools (e.g., Jupyter) facilitate systematic transformations at the cost of higher cognitive load. The results uncover systematic trade-offs among tools, indicating no single optimal choice and underscoring the critical role of interface design in shaping data work practices.

Current AI alignment approaches rely on static human preferences, which struggle to capture the dynamic, context-dependent nature of human–AI collaboration. This work proposes a paradigm shift toward “interactive complementarity,” emphasizing that preferences emerge dynamically through the co-evolution of human and AI behaviors. Introducing a trajectory-level perspective on dynamic alignment, the study integrates machine learning with social science theories and insights from interdisciplinary workshops to construct a framework for modeling human–AI interaction dynamics. This framework reveals novel forms of asymmetry and coordination challenges inherent in such systems. By establishing an alignment agenda tailored to dynamic human–AI workflows, the research lays a theoretical foundation for developing AI systems capable of interactive adaptation.

This work addresses the long-term challenge of aligning artificial intelligence with human values by proposing a scalable modeling framework grounded in social physics. Integrating dynamical systems analysis with multi-agent simulation, the framework investigates the evolution of social norms under widespread adoption of personalized AI assistants. Positioning social physics as a cognitive bridge, it enables prospective assessment of AI’s macro-level societal impacts and reveals that non-adaptive alignment mechanisms risk value lock-in and normative collapse. Through experiments across diverse initial conditions, the study delineates trajectories of norm evolution, demonstrating that adaptive alignment is crucial for preserving value diversity. The proposed approach offers a novel, efficient, and quantifiable paradigm for evaluating the societal implications of AI deployment.