Our Theater of Anonymity.

Ethics review boards are increasingly asked to review big health data research proposals using a regulatory framework written prior to the current era of machine learning and artificial intelligence. Traditional consideration of individual identifiability does not account for the growing recognition that almost all data can be reidentified. This leaves the research ethics community performing a "theater of anonymity": weighing benefit versus risk on the inclusion of participant identifiers alone.

Transparency and ongoing communication with participants in brain organoid research: Consensus of an interdisciplinary working group.

Stem cell-based models of the human brain benefit from biospecimens that can be used for a broad range of future research. But current regulations do not address the desire of research participants to remain engaged beyond initial biospecimen donation. We present practicable strategies for engaging participants while preserving scientific potential.

Dying in Darkness: Deviations From Data Sharing Ethics in the US Public Health System and the Data Genocide of American Indian and Alaska Native Communities.

Tribal governments and Tribal Epidemiology Centers face persistent challenges in obtaining the public health data that are essential to accomplishing their legal and ethical duties to promote health in American Indian and Alaska Native communities. We assessed the ethical implications of current impediments to data sharing among federal, state, and Tribal public health partners. Public health ethics obligates public health data sharing and opposes data collection without dissemination to affected communities.

Long-Term Engagement of Diverse Study Cohorts in Decentralized Research: Longitudinal Analysis of "All of Us" Research Program Data.

Background: The generalizability of clinical research hinges on robust study designs, which include the recruitment and maintenance of a representative study population. This study examines the evolution of the demographic characteristics of 329,038 participants who enrolled and participated in The All of Us Research Program (AoURP), a decentralized study aimed at representing the diversity of the United States.

Objective: The primary objectives of this study were to assess alterations in the demographic composition of the cohort at different protocol stages within AoURP, while analyzing completion rates and timeframes for survey and substudy completion.

Synthetic Health Data: Real Ethical Promise and Peril.

Researchers and practitioners are increasingly using machine-generated synthetic data as a tool for advancing health science and practice, by expanding access to health data while-potentially-mitigating privacy and related ethical concerns around data sharing. While using synthetic data in this way holds promise, we argue that it also raises significant ethical, legal, and policy concerns, including persistent privacy and security problems, accuracy and reliability issues, worries about fairness and bias, and new regulatory challenges. The virtue of synthetic data is often understood to be its detachment from the data subjects whose measurement data is used to generate it.

AI Governance: A Challenge for Public Health.

The rapid evolution of artificial intelligence (AI) is structuralizing social, political, and economic determinants of health into the invisible algorithms that shape all facets of modern life. Nevertheless, AI holds immense potential as a public health tool, enabling beneficial objectives such as precision public health and medicine. Developing an AI governance framework that can maximize the benefits and minimize the risks of AI is a significant challenge.

The Key Features of a Genetic Nondiscrimination Policy: A Delphi Consensus Statement.

Importance: Governments worldwide have become increasingly cognizant of the spread of genetic discrimination (negative treatment or harm on the basis of actual or presumed genetic characteristics). Despite efforts by a number of governments to establish regulations addressing this phenomenon, public concern about genetic discrimination persists.

Objective: To identify key elements of an optimal genetic nondiscrimination policy and inform policymakers as they seek to allay genetic nondiscrimination and related public anxieties.

An Application of Evidence-Based Approaches to Engage Young People in the Design of a Global Mental Health Databank.

Introduction: Engaging youth in mental health research and intervention design has the potential to improve their relevance and effectiveness. Frameworks like Roger Hart's ladder of participation, Shier's pathways to participation and Lundy's voice and influence model aim to balance power between youth and adults. Hart's Ladder, specifically, is underutilized in global mental health research, presenting new opportunities to examine power dynamics across various contexts.

Translational Research and Communities.

Impactful translational research requires new approaches to computational analysis and bioethics, both of which have been advanced by adoption of community-engagement strategies. Community knowledge and experience will hone data collection, research, and insights and accelerate the impact of derived translational applications to improve individual health, medical decision-making, and public health policy. In the context of translational research with big health data, meaningful community-researcher engagement will require developing and deploying coengagement tools across the research life cycle and developing approaches for novel coproduction.

Young people's data governance preferences for their mental health data: MindKind Study findings from India, South Africa, and the United Kingdom.

Mobile devices offer a scalable opportunity to collect longitudinal data that facilitate advances in mental health treatment to address the burden of mental health conditions in young people. Sharing these data with the research community is critical to gaining maximal value from rich data of this nature. However, the highly personal nature of the data necessitates understanding the conditions under which young people are willing to share them.

Big Health Data Research and Group Harm: The Scope of IRB Review.

Much of precision medicine is driven by big health data research-the analysis of massive datasets representing the complex web of genetic, behavioral, environmental, and other factors that impact human well-being. There are some who point to the Common Rule, the regulation governing federally funded human subjects research, as a regulatory panacea for all types of big health data research. But how well does the Common Rule fit the regulatory needs of this type of research? This article suggests that harms that may arise from artificial intelligence and machine-learning technologies used in big health data research-and the increased likelihood that this research will affect public policy-mean it is time to consider whether the current human research regulations prohibit comprehensive, ethical review of big health data research that may result in group harm.

Engaged genomic science produces better and fairer outcomes: an engagement framework for engaging and involving participants, patients and publics in genomics research and healthcare implementation.

Genomic science is increasingly central to the provision of health care. Producing and applying robust genomics knowledge is a complex endeavour in which no single individual, profession, discipline or community holds all the answers.  Engagement and involvement of diverse stakeholders can support alignment of societal and scientific interests, understandings and perspectives and promises better science and fairer outcomes.

U.S. Adult Perspectives on Facial Images, DNA, and Other Biometrics.

Applications of biometrics in various societal contexts have been increasing in the United States, and policy debates about potential restrictions and expansions for specific biometrics (such as facial recognition and DNA identification) have been intensifying. Empirical data about public perspectives on different types of biometrics can inform these debates. We surveyed 4048 adults to explore perspectives regarding experience and comfort with six types of biometrics; comfort providing biometrics in distinct scenarios; trust in social actors to use two types of biometrics (facial images and DNA) responsibly; acceptability of facial images in eight scenarios; and perceived effectiveness of facial images for five tasks.

GA4GH: International policies and standards for data sharing across genomic research and healthcare.

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution.

A survey of U.S. public perspectives on facial recognition technology and facial imaging data practices in health and research contexts.

Facial imaging and facial recognition technologies, now common in our daily lives, also are increasingly incorporated into health care processes, enabling touch-free appointment check-in, matching patients accurately, and assisting with the diagnosis of certain medical conditions. The use, sharing, and storage of facial data is expected to expand in coming years, yet little is documented about the perspectives of patients and participants regarding these uses. We developed a pair of surveys to gather public perspectives on uses of facial images and facial recognition technologies in healthcare and in health-related research in the United States.

Results of the Genetic Counselor SARS-CoV-2 Impact Survey from the National Society of Genetic Counselors: Progress and penalty during the COVID-19 pandemic.

The Genetic Counselor SARS-CoV-2 Impact Survey (GCSIS) describes the impact of the pandemic on genetic counselors and genetic counseling services. With this information, the National Society of Genetic Counselors (NSGC) can better: (1) support advocacy and access efforts for genetic counseling services at both federal- and state-level; (2) promote effective billing and reimbursement for genetic counseling services provided via telemedicine; and (3) make decisions about how to best support genetic counselors. The survey was hosted on a novel data collection and analysis platform from LunaDNA and was open to all genetic counselors (n = 5,531 based on professional society membership).

Assessment of the research program's informed consent process.

Informed consent is the gateway to research participation. We report on the results of the formative evaluation that follows the electronic informed consent process for the Research Program. Of the nearly 250,000 participants included in this analysis, more than 95% could correctly answer questions distinguishing the program from medical care, the voluntary nature of participation, and the right to withdraw; comparatively, participants were less sure of privacy risk of the program.

Research ethics in a pandemic: considerations for the use of research infrastructure and resources for public health activities.

The number and size of existing research studies with massive databases and biosample repositories that could be leveraged for public health response against SARS-CoV-2 (or other infectious disease pathogens) are unparalleled in history. What risks are posed by coopting research infrastructure-not just data and samples but also participant recruitment and contact networks, communications, and coordination functions-for public health activities? The case of the Seattle Flu Study highlights the general challenges associated with utilizing research infrastructure for public health response, including the legal and ethical considerations for research data use, the return of the results of public health activities relying upon research resources to unwitting research participants, and the possible impacts of public health reporting mandates on future research participation. While research, including public health research, is essential during a pandemic, careful consideration should be given to distinguishing and balancing the ethical mandates of public health activities against the existing ethical responsibilities of biomedical researchers.

Developing pathways for community-led research with big data: a content analysis of stakeholder interviews.

Background: Big data (BD) informs nearly every aspect of our lives and, in health research, is the foundation for basic discovery and its tailored translation into healthcare. Yet, as new data resources and citizen/patient-led science movements offer sites of innovation, segments of the population with the lowest health status are least likely to engage in BD research either as intentional data contributors or as 'citizen/community scientists'. Progress is being made to include a more diverse spectrum of research participants in datasets and to encourage inclusive and collaborative engagement in research through community-based participatory research approaches, citizen/patient-led research pilots and incremental research policy changes.

Bringing Code to Data: Do Not Forget Governance.

Developing or independently evaluating algorithms in biomedical research is difficult because of restrictions on access to clinical data. Access is restricted because of privacy concerns, the proprietary treatment of data by institutions (fueled in part by the cost of data hosting, curation, and distribution), concerns over misuse, and the complexities of applicable regulatory frameworks. The use of cloud technology and services can address many of the barriers to data sharing.

Who Are the People in Your Neighborhood? Personas Populating Unregulated mHealth Research.

A key feature of unregulated mHealth research is the diversity of participants in this space. Applying an approach drawn from user experience design, we describe a set of archetypal unregulated mHealth researcher "personas," which range from individuals who seek empowerment or have philanthropic objectives to those who are primarily motivated by financial gain or have misanthropic objectives. These descriptions are useful for evaluating policies applicable to mHealth to understand how they will impact various stakeholders.

Unregulated Health Research Using Mobile Devices: Ethical Considerations and Policy Recommendations.

Mobile devices with health apps, direct-to-consumer genetic testing, crowd-sourced information, and other data sources have enabled research by new classes of researchers. Independent researchers, citizen scientists, patient-directed researchers, self-experimenters, and others are not covered by federal research regulations because they are not recipients of federal financial assistance or conducting research in anticipation of a submission to the FDA for approval of a new drug or medical device. This article addresses the difficult policy challenge of promoting the welfare and interests of research participants, as well as the public, in the absence of regulatory requirements and without discouraging independent, innovative scientific inquiry.

Erratum: Publisher Correction: Modernizing and designing evaluation frameworks for connected sensor technologies in medicine.

[This corrects the article DOI: 10.1038/s41746-020-0237-3.].