ethical research practices

This study addresses the absence of unified and transparent research ethics guidelines in top-tier security and privacy conferences, which has led to ambiguous review criteria and inconsistent enforcement, thereby hindering the community’s ethical awareness. Through a systematic analysis of ethics policies across four leading conferences over multiple years and semi-structured interviews with 20 researchers, this work presents the first comprehensive account of the evolution of ethical practices in the field, identifying a critical gap in ethics education as the primary bottleneck. Drawing on qualitative findings and principles of community-based participatory design, the paper proposes an innovative framework featuring an inter-conference coordination mechanism and an open Ethics Wiki. It delineates current progress and key barriers to consensus-building and has already launched the Ethics Wiki as an initial step toward collaborative governance.

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.

Current ethical review systems struggle to address structural ethical risks in large-scale, interdisciplinary research due to insufficient capacity, inconsistent standards, and privacy constraints. This work proposes Mirror, a multi-agent framework that integrates normative understanding, an executable rule repository, and a multi-role collaborative deliberation mechanism to support both rapid compliance checks and in-depth committee-like evaluations. Leveraging a newly constructed domain-specific ethical QA dataset, EthicsQA, the authors fine-tune a specialized language model, EthicsLLM, and integrate it with a rule engine and a structured ethical dimension assessment framework. Experimental results demonstrate that the proposed approach significantly outperforms general-purpose large language models in evaluation quality, consistency, and domain expertise, making it suitable for projects ranging from minimal-risk studies to complex scientific endeavors.

Current assessments of the integrity of randomized controlled trials (RCTs) rely heavily on manual processes that are complex, subjective, and prone to inconsistency, thereby compromising the quality of evidence-based guidelines. To address this limitation, this work proposes INSPECT-AI, a novel framework that integrates large language models (LLMs) with a knowledge graph grounded in the RIPE-O ontology (RIPE-KG) to automate integrity evaluation, standardize semantic interpretation, and enable full auditability. The authors constructed a RIPE-KG comprising 95 RCTs annotated by experts across 140 assessment criteria and demonstrated that LLM-augmented evaluation significantly enhances efficiency, inter-rater consistency, and traceability. This approach establishes a transparent, reproducible paradigm for evidence synthesis in systematic reviews and guideline development.

This study addresses the urgent governance gap exposed by the militarization of large reasoning models, where researchers lack effective means to prevent harmful deployments. It introduces the concept of institutional veto power into AI governance for the first time, drawing on precedents from nuclear non-proliferation and biomedical ethics to design an enforceable governance framework led by communities most vulnerable to militarized AI. By identifying critical veto points across the model development lifecycle, the paper proposes embedded, institutionally anchored mechanisms resistant to political manipulation. This approach shifts AI governance from symbolic safeguards toward substantive accountability, offering a viable pathway toward AI “disarmament.”

Current medical research agents lack domain-specific evaluation mechanisms that rigorously assess scientific validity, methodological soundness, reproducibility, and boundary safety. This work proposes MedSkillAudit—the first skill auditing framework tailored for medical research agents—which employs a hierarchical, structured pipeline to evaluate skill readiness prior to deployment. The framework incorporates expert double-blind scoring (0–100), tiered release recommendations, and high-risk flags, and quantifies agreement between the system and human experts using ICC(2,1) and weighted Cohen’s kappa. Evaluated on 75 skills, the system achieved an ICC of 0.449, surpassing inter-human rater agreement (ICC = 0.300) and demonstrating closer alignment with consensus scores (SD = 9.5 vs. 12.4), thereby validating its effectiveness and reliability.

This study addresses the prevailing overemphasis on legal compliance in student data governance within learning analytics, which often neglects critical ethical dimensions such as fairness, student autonomy, accountability, and educational purpose. To bridge this gap, the work proposes LEAGUE—a six-pillar ethical governance framework encompassing Legitimacy, Equity, Autonomy, Governance, Utility, and Ethics-by-Design—integrating insights from learning analytics, data ethics, and capability-oriented theories of educational justice. Moving beyond conventional compliance paradigms, this framework pioneers the application of value-sensitive design in the field. Through conceptual review, interdisciplinary theoretical synthesis, and a case analysis of an early warning system, the study demonstrates the framework’s feasibility in enhancing transparency, educational meaningfulness, and ethical justifiability, offering a theoretically grounded yet practically actionable pathway for ethically robust learning analytics.