participant recruitment

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.

To address challenges in clinical trials—including manual protocol execution, poor real-time responsiveness, and delayed adherence monitoring—this paper proposes a personalized participant agent system integrating finite-state machines (FSMs) with large language models (LLMs). The system employs interpretable FSMs to formalize protocol logic, while leveraging LLMs for structured data extraction and adaptive intervention decisions during conversational interactions. It further enables real-time protocol compliance verification and end-to-end behavioral audit tracing. We introduce the first closed-loop protocol execution paradigm combining “FSM-driven control” with “LLM-enhanced reasoning,” ensuring traceable participant behavior, context-aware interventions, and verifiable protocol logic. Evaluated in multicenter, time-sensitive trials, the system significantly reduces data entry error rates, improves adherence monitoring latency to sub-second granularity, and cuts human supervisory costs by over 70%.

This study addresses structural barriers—misaligned incentives, divergent temporal horizons, and resource constraints—that impede cross-sector collaboration among academia, industry, government, and NGOs in trust and security domains. We propose a collaborative framework centered on “goal alignment, role clarification, and trust co-construction.” Innovatively, we introduce the concept of “expressive work,” foregrounding meaning negotiation and value embodiment in intersectoral communication to foster inclusive, transparent, and contextually grounded collaboration. Methodologically, we integrate multi-stakeholder engagement, comparative case analysis, and cross-sector priority mapping to develop a reusable implementation pathway. The framework demonstrably enhances coordination efficiency across diverse actors and provides a methodological guide for interdisciplinary research in high-stakes domains—balancing ethical rigor with practical feasibility. (124 words)

Cybersecurity researchers often lack actionable frameworks for stakeholder-oriented ethical analysis. This paper proposes a systematic stakeholder analysis methodology that categorizes stakeholders into four archetypal groups—primary users, secondary affected parties, governance entities, and the general public—and maps them onto empirical research techniques including semi-structured interviews, scenario modeling, and risk mapping, illustrated with real-world case studies. The framework bridges a critical gap in cybersecurity ethics practice by enabling methodologically grounded, context-sensitive ethical assessment. Evaluation demonstrates that research teams using the framework achieve significantly improved efficiency and accuracy in identifying ethical risk exposure points across the project lifecycle, thereby enhancing the rigor, reproducibility, and practical applicability of ethical review. It notably strengthens systematicity in ethical reasoning, improves risk detection precision, and supports more robust, evidence-informed ethical decision-making.

This study addresses critical challenges in deploying AI in healthcare across low-resource countries—such as Nepal and Ghana—including data privacy risks, insufficient model reliability, absence of ethical oversight, and inadequate localization of governance frameworks. We conducted a mixed-methods empirical investigation, comprising 217 surveys and 43 in-depth interviews with clinicians, policymakers, and community stakeholders. Building on these findings, we propose the first responsible AI in health framework specifically designed for low-resource settings, integrating context-sensitive ethical governance, tiered compliance mechanisms, and community co-verification pathways. Results indicate that 85% of respondents emphasized the necessity of ethical oversight, while 72% advocated for nationally embedded regulatory bodies. The framework demonstrably identifies and mitigates algorithmic bias, enhances cross-cultural adaptability, and strengthens systemic trust. It offers a transferable methodological paradigm and actionable implementation template for AI governance in Global South health systems.

This study examines how the federally mandated Homeless Management Information System (HMIS) data infrastructure, implemented within a context of compulsory interorganizational collaboration in U.S. homeless services, simultaneously enhances cross-agency coordination and reinforces power asymmetries. Drawing on in-depth interviews with six domain experts, the analysis reveals that while standardization improves collaborative efficiency and knowledge sharing, disparities in resources, analytical capacity, and institutional authority render less powerful participants prone to passive compliance, thereby diminishing their influence in decision-making. The findings demonstrate that mandatory data-sharing regimes can entrench structural inequities, offering critical theoretical insights and practical guidance for designing more equitable public data infrastructures.

This study addresses the invisibility and precarity of African data laborers in the digital isolation era, particularly within globally outsourced content moderation and data annotation—sectors characterized by low visibility and inadequate labor protections. Employing a mixed-methods approach over nine months, it integrated desk research, a 43-country survey (n=81), multi-sited ethnographic observation, and participatory action research. It produced the first comprehensive mapping of Africa’s content moderation industry, identifying 17 firms serving predominantly Western clients. Drawing on Honneth’s theory of recognition, the study uncovered workers’ fundamental demands for occupational dignity and institutional acknowledgment. It advanced a collective-action-oriented participatory research paradigm to center worker agency. Findings confirmed widespread psychological support deficits and chronic occupational insecurity. By rigorously documenting structural inequities, the research elevated data labor rights onto the global technology governance agenda, contributing both empirically grounded policy insights and a methodological framework for ethical, decolonial AI labor studies.

This study examines how the U.S. Food and Drug Administration’s new food traceability rule transforms small-scale agricultural producers into uncompensated data laborers, exacerbating existing burdens related to labor, financial resources, and technological capacity. Drawing on data feminist theory—an approach newly applied to food regulatory policy analysis—the research employs qualitative coding of 1,198 public comments to systematically uncover structural inequities embedded in the rule’s implementation. The analysis identifies three core tensions: the invisible burden of data labor imposed on marginalized actors, the technical infeasibility of mandated tracking systems for small operations, and regulatory ambiguity that fuels inconsistent enforcement. These findings offer both empirical evidence and theoretical innovation to inform the development of more inclusive and equitable data governance frameworks in food safety regulation.

High failure rates in data science projects stem primarily from the lack of integrated governance addressing technical, organizational, and ethical risks. This study employs a systematic literature review and cross-framework content analysis to compare mainstream risk management standards—including ISO 31000, PMBOK, NIST RMF, and CRISP-DM—assessing their adequacy across the data science lifecycle. It identifies critical structural gaps in handling data maturity, cross-functional collaboration, and socio-technical risk responsiveness. To address these limitations, the paper proposes an innovative “Governance–Monitoring–Ethics” triadic framework that transcends unidimensional risk models by embedding ethical review and continuous governance mechanisms directly into technical workflows and organizational practices. The resulting integrated risk management perspective supports responsible data practices and establishes a theoretical foundation for subsequent framework development and empirical validation. (149 words)

This study introduces the novel concept of “clinical trial engineering”—the systematic manipulation of statistical analyses to generate misleading clinical trial evidence in support of drug approval, distinct from conventional paper mills. Focusing on 23 studies linked to Iran’s CinnaGen and its subsidiary Orchid Pharmed, the authors applied the INSPECT-SR credibility framework, integrating PubMed literature screening, raw data verification, and co-authorship network analysis to systematically evaluate evidentiary reliability. The investigation uncovered 180 issues spanning nine categories of systemic bias, including incomplete reporting, arithmetic errors, and design flaws. These findings reveal a structural pattern of research manipulation driven by commercial pressures, publication incentives, and permissive regulatory pathways, prompting regulatory agencies to reassess the credibility of the associated clinical evidence.