human subjects research

Social challenge studies—such as online experiments exposing participants to harmful content—lack systematic ethical guidelines; risk mitigation and oversight mechanisms remain markedly underdeveloped compared to established frameworks in medical challenge research. Method: This paper systematically adapts the mature ethical framework of medical challenge studies to computational social science, integrating interdisciplinary analysis to develop context-sensitive ethical principles for online environments and proposing a novel assessment mechanism for long-term latent harms. Contribution/Results: It establishes the first operational ethical standards system specifically designed for social challenge research, thereby addressing a critical regulatory gap. It advances institutionalized ethics review processes tailored to digital experimentation and catalyzes scholarly discourse on risk governance in digital research contexts. By bridging disciplinary divides, the work provides actionable guidance for researchers, ethics boards, and platform partners navigating ethically complex online interventions.

Observational studies in social VR face a structural tension among observer visibility, data traceability, and participant autonomy—challenges inadequately addressed by conventional public-space ethics frameworks. This paper conducts a narrative literature review of HCI scholarship on ethical observational research in digital environments, synthesizing insights to propose five domain-specific ethical guidelines for public social VR settings. Its contributions are threefold: first, it introduces *observer visibility* as a core ethical dimension—an analytical innovation not previously foregrounded in VR ethics; second, it advocates replacing static, one-time informed consent with platform-enabled design interventions and community-engaged, iterative consent processes; third, it develops a pragmatic yet theoretically grounded ethical assessment framework. The resulting principles provide systematic guidance for VR research governance, platform policy development, and ethical practice by researchers.

The widespread application of eye-tracking technology in high-stakes domains—such as healthcare and marketing—exposes a critical gap in researchers’ ethical reflexivity, particularly regarding privacy and responsible conduct. Method: To address this, we developed the first dual-modal assessment framework: REFLECT (a qualitative questionnaire) and SPERET (a psychometrically validated quantitative scale), designed to systematically measure researchers’ reflexivity concerning privacy and ethics. Grounded in cross-institutional expert collaboration and rigorous scale development, the tools were empirically validated through surveys involving over 70 eye-tracking researchers. Contribution/Results: Results indicate that researchers generally possess awareness of privacy risks and methodological limitations; moreover, ethical responsibility increases significantly with project experience. This study pioneers an operationalized, quantifiable assessment of ethical reflexivity in eye-tracking research, offering both methodological rigor and practical guidance for human factors research in high-risk domains.

Internet measurement research confronts pressing ethical challenges—including privacy risks, informed consent complexities, potential participant harm, and misalignment between conventional ethics review frameworks and technical practice—yet scholarly understanding of researchers’ lived ethical decision-making remains limited. This study employs thematic analysis of in-depth interviews with 16 internet measurement researchers across the EU, grounded in a case-study framework. It identifies five recurrent ethical challenge categories and corresponding mitigation strategies, and introduces the novel concept of “ethical craft knowledge,” highlighting the central role of mentorship and peer collaboration in cultivating situated ethical practice. Findings reveal that institutional review boards frequently lack technical literacy regarding measurement methodologies, while cross-institutional and cross-jurisdictional regulatory fragmentation imposes substantial invisible labor. The study calls for a discipline-specific, technically informed ethics support infrastructure tailored to the epistemic and operational realities of internet measurement.

To address inconsistencies in ethical standards, protracted review processes, and variable assessment quality in Institutional Review Board (IRB) oversight, this paper introduces IRB-LLM—the first domain-specific large language model designed explicitly for IRBs. IRB-LLM integrates domain-adaptive fine-tuning, retrieval-augmented generation (RAG), and multi-task prompt engineering to establish a dynamic human-AI collaborative decision-making framework capable of semantically modeling ethical texts. It delivers three core functionalities: pre-review screening, consistency verification, and decision support—collectively enhancing both efficiency and standardization of ethical review. Experimental evaluation demonstrates that IRB-LLM reduces average processing time by 32% and improves inter-reviewer consistency in feedback by 27%. The model provides a reproducible, empirically validated paradigm for AI-augmented governance of research ethics.

This study addresses the frequent neglect of environmental impacts in computationally intensive research—such as artificial intelligence—due to ambiguous ethical review policies. It presents the first systematic framework integrating environmental sustainability into the ethical oversight of computational research. By delineating clear review boundaries, establishing evidentiary standards, and developing researcher self-assessment tools, the framework enables institutional ethics committees to effectively evaluate the environmental costs of proposed projects. This approach provides actionable guidance for ethical review processes and encourages researchers to proactively consider the ecological footprint of their work during early design stages, thereby addressing a critical gap in current research ethics frameworks concerning sustainability.

This study addresses the ethical dilemmas in data visualization arising from contextual constraints that prevent full disclosure of raw data, reconceptualizing data disclosure ethics as a multi-stakeholder negotiation process rather than attributing issues to individual deception or misunderstanding. To explore ethical communication mechanisms, we designed and open-sourced Purrsuasion, an educational game in which students assume roles as constrained data providers and information seekers, engaging in iterative negotiation. Integrating mixed-methods analysis, gamified platform development, heuristic rubrics, and user interaction logs, our findings reveal that learners often settle on suboptimal visual designs and struggle to accurately infer authors’ intentions when envisioning ideal visualizations. Building on these insights, we propose a heuristic scoring framework to support socio-technical judgment, offering a novel pathway for ethics education and practice in data visualization.

This study addresses the persistent gap between users’ willingness and actual behavior in data donation practices, focusing on how the presentation of personal data influences donation decisions—a dimension underexplored from a design-oriented perspective. Through a real-world experiment (N=24), the research evaluates three pre-donation data exploration frameworks: “self-focused,” “social comparison,” and “collective uniqueness.” Findings reveal that the “social comparison” frame significantly increases donation rates to 87.5%, outperforming the “self-focused” condition (62.5%), whereas the “collective uniqueness” frame reduces donations to 37.5% due to induced cognitive confusion and heightened privacy concerns. This work pioneers the integration of behavioral design into public-sector data donation, uncovering a pronounced framing effect in data selection and underscoring the critical role of interface design in fostering meaningful user participation.

This study addresses the “protection paradox” wherein AI-driven data analytics, while intended to safeguard vulnerable populations, may inadvertently exacerbate their vulnerability through inherent technical processes. Conceptualizing vulnerability as a dynamically constructed outcome of data practices, the work innovatively integrates ethical considerations into four critical stages of the AI pipeline: dataset design, operationalization and modeling, inferential logic, and dissemination strategies. Employing AI for Social Good (AI4SG) methodologies—including computer vision, critical dataset analysis, and inference auditing—the research identifies four key factors that contribute to algorithmic fragility. Building on these insights, the authors propose a reflexive ethical roadmap that enables researchers to navigate platform-based data studies while mitigating risks of computational exposure and exploitation stemming from well-intentioned interventions.