historical contextualization

Existing dataset documentation tools struggle to achieve real-world adoption due to ambiguous value propositions, misalignment with practical contexts, insufficient attention to human labor costs, and a lack of systemic integration. This study addresses these challenges through a mixed-methods systematic scoping review of 59 relevant publications, combining qualitative coding with quantitative synthesis to uncover the underlying motivations driving tool design and their relationship to institutional norms. The analysis identifies four key patterns that hinder adoption and advances a responsible AI design perspective that shifts emphasis from individual accountability to institutional solutions. The work advocates embedding sustainable documentation practices within organizational workflows and cultures, offering the HCI community actionable pathways toward institutionalizing responsible data stewardship.

This work addresses the tension between data systems’ capacity to reinforce memory and their inability to support socially necessary forms of trustworthy “forgetting,” such as compliant data deletion, harm mitigation, and removal of harmful content. It reconceptualizes forgetting as a sociotechnical practice encompassing erasure, machine unlearning, and exclusion, highlighting its multidimensional nature across agency, temporality, reversibility, and scale. For the first time, forgetting is positioned as a first-class capability within knowledge infrastructures. Integrating machine unlearning, semantic data dependency management, participatory modeling, and large-scale manipulation analysis, the study proposes a governance framework centered on transparency, accountability, and epistemic justice. The analysis reveals that forgetting can be both empowering and silencing, offering a theoretical foundation for designing mechanisms that balance regulatory compliance, system utility, and fairness.

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.

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.

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 investigates the mechanisms underlying the emergence of social bias in artificial intelligence systems, practitioners’ understandings of these issues, and potential mitigation strategies. Employing a qualitative multiple-case design grounded in an interpretivist paradigm, the research integrates intersectionality theory and cognitive science, drawing on semi-structured interviews, document analysis, and triangulation to examine AI practitioners’ experiences across design, development, and governance. Findings reveal that algorithmic bias is deeply rooted in historical inequities, exclusionary assumptions, and organizational pressures for efficiency, underscoring the insufficiency of purely technical fixes. The study proposes an innovative approach that embeds ethical considerations early in the development lifecycle, strengthens structural accountability, fosters diverse stakeholder participation and cognitive awareness, and actively reshapes organizational culture to cultivate AI systems that are transparent, accountable, and aligned with community values.

This study addresses the persistent marginalization of voices from communities most harmed by online harms in conventional data work, which often lacks mechanisms for accountability and equitable redress. Drawing on a Science and Technology Studies (STS) perspective, the research employs ethnographic methods to examine a feminist civic technology initiative that collaborates with affected communities to co-construct a dataset grounded in restorative justice principles. By reframing data production as a practice of repair and reparation, the project reconfigures power and responsibility relations in AI data labor through collective governance. The findings illuminate key challenges in achieving fair compensation and collaborative governance, offering a critical, affected-centered, and repair-oriented pathway toward responsible AI development.

This study addresses the challenge of identifying and measuring erasure harms—a form of representational harm in natural language processing (NLP) systems—that remains poorly understood due to the absence of a clear, unified conceptual framework. To tackle this issue, the paper develops a structured and operational definition of erasure harms through conceptual analysis and ethical reasoning, delineating its core components and criteria for judgment. The proposed framework overcomes limitations of existing definitions, which are either overly broad or confined to specific contexts, thereby offering a robust theoretical foundation and practical guidance for systematically recognizing, evaluating, and mitigating erasure harms across diverse NLP applications.

This study addresses the prevalent fragmentation in AI education, where technical knowledge, societal impact, and workplace competencies are often taught in isolation. To bridge this gap, the authors propose an innovative curriculum mapping framework that systematically integrates three dimensions: AI technical foundations, societal harms, and professional competencies. Leveraging both institutional course offerings and an external repository of 335 registered courses, the framework enables cross-curricular content analysis. An initial analysis of six courses reveals relatively comprehensive coverage of technical content, uneven attention to societal harms, and minimal explicit assessment of workplace competencies. The proposed framework thus offers both a methodological foundation and empirical evidence for designing holistic AI literacy education that cultivates responsible agency alongside technical proficiency.

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.