ensure ethical compliance

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 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.

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.

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.

This work addresses the current lack of a systematic framework for evaluating ethical risks in data collection practices for large language models (LLMs). It proposes the first quantifiable assessment framework that integrates multiple prominent ethical theories, structuring evaluation around core ethical principles through a set of targeted questions and establishing a scoring system to measure ethical risk. This approach enables systematic, quantitative ethical auditing of LLM data curation processes. By offering a practical tool for assessing ethical compliance in AI development, the framework fills a critical gap in existing research—particularly in the integration of diverse ethical theories and the empirical evaluation of real-world data practices—thereby advancing the responsible development of artificial intelligence.

This study addresses the widespread omission of ethical approval and informed consent disclosures in IEEE VIS research, which undermines research reproducibility and jeopardizes the rights of both participants and researchers. Conducting the first large-scale systematic content analysis of 255 TVCG-published VIS papers, this work employs manual coding and quantitative analysis to reveal that only 3.2% of studies involving human participants fully reported both ethical approval and informed consent. The findings expose a critical lack of ethical transparency in the VIS community, underscoring the urgent need for standardized ethical reporting guidelines. By providing empirical evidence and foundational insights, this research offers a crucial basis for advancing ethical infrastructure within the discipline.

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.