Public domain human genomic resources are infrastructure: tools researchers use to ask basic biological questions whose answers are then translated into products and care. Translational science now asks them to support an expanding set of tasks, including clinical variant interpretation for diverse populations, pharmacogenomic prescribing, polygenic risk prediction, and the training of clinical artificial intelligence. The corpus of public domain genomes, due in large part to upstream recruitment choices, is not fit for these purposes, and the gap between discovery and translation is widening. This essay argues that closing the gap requires treating public domain genomic infrastructure as a particular object of translational bioethics rather than a technical precondition for it. The limited number of genomes in the public domain relative to the broader genomic record, and the typology-friendliness of how that record represents human variation, are two faces of the same set of upstream choices. Reversing them is not a matter of more sampling under existing terms; it is a matter of building infrastructure of a particular kind; infrastructure made from people. That category, common in genomics but absent from the rest of science, demands an ethical apparatus the field has not yet built. Here we consider the commitments such an apparatus requires, and argue that where, how, and with whom we build genomic infrastructure is itself an ethics question the field has largely declined to ask.
The rapid growth of artificial intelligence and automated systems has significantly impacted digital survey-based research, particularly when recruitment is conducted via social media platforms. This case study details the erosion of data integrity in a 2025 follow-up survey, originally designed to replicate a successful 2021 study. Despite implementing increasingly sophisticated antifraud mechanisms such as CAPTCHA, IP geo-restrictions, and behavioral validation, survey bots repeatedly infiltrated the study, rendering the dataset unusable. This highlights a critical tension between broad digital accessibility and the vulnerability of online research to fraud. Moreover, the use of financial incentives to participate in research, while effective in increasing response rates, was found to amplify bot activity and raise ethical dilemmas surrounding compensation and data quality. As automated deception continues to evolve, researchers must navigate a complex landscape where conventional protections are no longer sufficient. The authors call for the urgent development of fieldwide standards, institutional support, and robust fraud prevention strategies to preserve the methodological and ethical integrity of digital research. Without such measures, the viability of online recruitment and the trustworthiness of the resulting data remain at serious risk.
Human-in-the-loop (HITL) approaches are commonly proposed to address alignment challenges arising from the use of large language models in research ethics oversight. This paper argues that, paradoxically, a HITL can amplify the risk of misalignment. Using the example of protocol triage in research ethics oversight, I demonstrate how reliance on imperfect proxies (observable stand-ins for ethical principles) creates a fundamental proxy-target gap in ethics use-cases. While human ethics committee reviewers are intended to supply phenomenological and causal judgments necessary to bridge this gap, their involvement inadvertently introduces new vulnerabilities: hallucination, over-reliance, reward hacking, and sycophancy. Each vulnerability arises directly from the interaction between human feedback signals and model optimization strategies. I outline key mitigation techniques, including retrieval grounding, calibration drills, structured prompting, and adversarial debate, explaining their distinct costs and benefits. Effective HITL oversight therefore demands specialized ethics committee reviewer competencies. These reviewers must recognize model vulnerabilities, understand mitigation trade-offs, and strategically deploy mitigations to preserve justificational alignment without sacrificing efficiency gains.
Our study examines the perspectives of researchers and other research team members who conducted National Institutes of Health (NIH)-funded studies about their use of a single institutional review board (sIRB) review and onboarding process. More than 84% (n = 639) of respondents agreed that NIH-sponsored multisite clinical trials benefit from sIRB use. A higher proportion of super-users (>10 studies) of an sIRB than moderate users (1-10 studies) described the onboarding process positively (n = 119, 56% vs. n = 225, 42%, p = .01) and found sIRBs to be more cost-effective than a site-specific model (n = 113, 53.05% vs. n = 221, 41%, p <.01). Timeline delays were experienced by over 90% (n = 680) of respondents and an increased administrative burden was reported by almost half (n = 353, 47%) of respondents. Respondents described increased administrative burden for sIRB approvals when an institution-specific application was required (n = 299, 51% vs. n = 30, 29%, p <.01). Most researchers judged sIRBs to be beneficial. However, delays in approval, increased administrative burden, and associated compulsory institution-specific applications posed logistical challenges for researchers. Reform efforts must focus on aligning sIRB and local study site standards to optimize sIRB study review.
Pregnant women have historically been excluded from clinical research, denying pregnant women the benefits of the societal investment in biomedical research and potential direct benefits from research participation. National and international guidance documents have called upon institutional review boards (IRBs) to institute policies to establish the expectation for inclusion of pregnant women in clinical research and require justification when pregnant women are excluded. However, only limited evidence exists regarding the extent to which IRBs have adopted policies consistent with these recommendations. We reviewed IRB policies and related guidance documents from the top 50 research institutions in the United States and the two largest independent IRBs. Four academic and one independent IRB included language that reinforced the view that pregnant women should not be included in research. Conversely, eight academic institutions included language that expressly fostered a culture of inclusion, and five academic institutions included language describing a requirement to justify decisions to exclude pregnant women. Neither independent IRB required justification for exclusion. While some institutions have implemented changes to advance the goal of ethical inclusion, considerable gaps remain between widely endorsed recommendations that IRBs contribute to facilitating appropriate inclusion of pregnant women in research and on-the-ground IRB policies.
