socio-technical analysis

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 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.

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 addresses the pervasive issue in computational social science wherein researchers’ considerable freedom in methodological choices often undermines the robustness of empirical conclusions, while computational failures are routinely overlooked. For the first time, the authors systematically apply a multiverse analysis framework to this domain, evaluating how diverse yet plausible analytical pipelines—including Bayesian inference, network generative modeling, and machine learning with and without large language models—affect the findings of three published studies. Their results demonstrate that substantive conclusions are highly sensitive to method selection. The work not only identifies specific method combinations prone to computational failure but also offers practical recommendations for selecting defensible analytical paths and transparently reporting the full spectrum of multiverse outcomes, underscoring the critical role of disclosing failed analyses in enhancing research reproducibility.

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.

This study addresses the overreliance on technical assessments in current AI safety and ethics research, which has led to insufficient understanding of human-AI interaction risks due to the neglect of empirical human-centered studies. Drawing on 93 expert surveys and 17 in-depth interviews spanning four communities—technical, socio-technical, governance, and normative—the research uncovers methodological tensions among scholars with different backgrounds, particularly highlighting the low engagement of technically oriented researchers with human-centered approaches. While the value of human research is widely acknowledged, its integration remains constrained by concerns about validity, scarce resources, methodological preferences, and inadequate infrastructural support. To counter superficial adoption—termed “human-washing”—the paper proposes actionable pathways for effectively incorporating human-centered research into AI safety practices, offering methodological guidance for interdisciplinary convergence.