Expanding the Agenda for a More Just Genomics.

The integration of genomics into public health and medicine is happening at a faster rate than the accrual of the capabilities necessary to ensure the equitable, global distribution of its clinical benefits. Uneven access to genetic testing and follow-up care, unequal distribution of the resources required to access and participate in research, and underrepresentation of some descent groups in genetic and clinical datasets (and thus uncertain genetic results for some patients) are just some of the reasons to center justice in genomics. A more just genomics is an imperative rooted in the ethical obligations incurred by a publicly funded science that is reliant on human data.

"Ethical Responsibility Very Often Gets Drowned Out": A Qualitative Interview Study of Genome Scientists' and ELSI Scholars' Perspectives on the Role and Relevance of ELSI Expertise.

Background: Genome scientists and Ethical, Legal, and Social Implications of genetics (ELSI) scholars commonly inhabit distinct research cultures - utilizing different research methods, asking different research questions, and valuing different types of knowledge. Collaborations between these two communities are frequently called for to enhance the ethical conduct of genomics research. Yet, little has been done to qualitatively compare genome scientists' and ELSI scholars' perspectives on collaborations with each other and the factors that may affect these collaborations.

Understanding the Gap: A Cross-Sectional Survey of ELSI Scholars' Dissemination Practices and Translation Goals.

Background: Researchers engaged in the study of the ethical, legal, and social implications (ELSI) of genetics and genomics are often publicly funded and intend their work to be in the public interest. These features of U.S.

Moral Engagement and Disengagement in Health Care AI Development.

Background: Machine learning (ML) is utilized increasingly in health care, and can pose harms to patients, clinicians, health systems, and the public. In response, regulators have proposed an approach that would shift more responsibility to ML developers for mitigating potential harms. To be effective, this approach requires ML developers to recognize, accept, and act on responsibility for mitigating harms.

The ELSI Virtual Forum, 30 Years of the Genome: Integrating and Applying ELSI Research.

This paper reports our analysis of the ELSI Virtual Forum: 30 Years of the Genome: Integrating and Applying ELSI Research, an online meeting of scholars focused on the ethical, legal, and social implications (ELSI) of genetics and genomics.

Developer Perspectives on Potential Harms of Machine Learning Predictive Analytics in Health Care: Qualitative Analysis.

Background: Machine learning predictive analytics (MLPA) is increasingly used in health care to reduce costs and improve efficacy; it also has the potential to harm patients and trust in health care. Academic and regulatory leaders have proposed a variety of principles and guidelines to address the challenges of evaluating the safety of machine learning-based software in the health care context, but accepted practices do not yet exist. However, there appears to be a shift toward process-based regulatory paradigms that rely heavily on self-regulation.

Stronger regulation of AI in biomedicine.

Regulatory agencies need to ensure the safety and equity of AI in biomedicine, and the time to do so is now.

To Understand Inequity, Bioethics Needs to Sort Things Out.

Bioethics is reexamining how to implement diversity, equity, inclusion, and justice concerns into scholarship. However, bioethicists should question the categories used to define diversity. The act of categorization is value laden, and classification systems confer power and benefits and generate harms.

Re-envisioning community genetics: community empowerment in preventive genomics.

As genomic technologies rapidly develop, polygenic scores (PGS) are entering into a growing conversation on how to improve precision in public health and prevent chronic disease. While the integration of PGS into public health and clinical services raises potential benefits, it also introduces potential harms. In particular, there is a high level of uncertainty about how to incorporate PGS into clinical settings in a manner that is equitable, just, and aligned with the long-term goals of many healthcare systems to support person-centered and value-based care.

Trustworthiness matters: Building equitable and ethical science.

Trustworthy science requires research practices that center issues of ethics, equity, and inclusion. We announce the Leadership in the Equitable and Ethical Design (LEED) of Science, Technology, Mathematics, and Medicine (STEM) initiative to create best practices for integrating ethical expertise and fostering equitable collaboration.

Not in my AI: Moral engagement and disengagement in health care AI development.

Machine learning predictive analytics (MLPA) are utilized increasingly in health care, but can pose harms to patients, clinicians, health systems, and the public. The dynamic nature of this technology creates unique challenges to evaluating safety and efficacy and minimizing harms. In response, regulators have proposed an approach that would shift more responsibility to MLPA developers for mitigating potential harms.

Values and Practices to Strengthen Genetic Research Partnerships with Indigenous Communities.

Genetic datasets lack diversity and include very few data from Indigenous populations. Research models based on equitable partnership have the potential to increase Indigenous participation and have led to successful collaborations. We report here on a meeting of participants in four Indigenous community-university partnerships pursuing research on precision medicine.

Words matter: The language of difference in human genetics.

Diversity, equity, and inclusion efforts in academia are leading publishers and journals to re-examine their use of terminology for commonly used scientific variables. This reassessment of language is particularly important for human genetics, which is focused on identifying and explaining differences between individuals and populations. Recent guidance on the use of terms and symbols in clinical practice, research, and publications is beginning to acknowledge the ways that language and concepts of difference can be not only inaccurate but also harmful.

Epistemic Rights and Responsibilities of Digital Simulacra for Biomedicine.

Big data and AI have enabled digital simulation for prediction of future health states or behaviors of specific individuals, populations or humans in general. "Digital simulacra" use multimodal datasets to develop computational models that are virtual representations of people or groups, generating predictions of how systems evolve and react to interventions over time. These include digital twins and virtual patients for clinical trials, both of which seek to transform research and health care by speeding innovation and bridging the epistemic gap between population-based research findings and their application to the individual.

A mixed-methods protocol to develop and validate a stewardship maturity matrix for human genomic data in the cloud.

This article describes a mixed-methods protocol to develop and test the implementation of a stewardship maturity matrix (SMM) for repositories which govern access to human genomic data in the cloud. It is anticipated that the cloud will host most human genomic and related health datasets generated as part of publicly funded research in the coming years. However, repository managers lack practical tools for identifying what stewardship outcomes matter most to key stakeholders as well as how to track progress on their stewardship goals over time.

Latinx attitudes, barriers, and experiences with genetic counseling and testing: A systematic review.

As genetics is increasingly used across clinical settings, there is a need to understand the impact and experiences of diverse patients. This review systematically examined research literature on Latinx experiences with genetic counseling and genetic testing (GC/GT) in the United States, synthesizing key themes and knowledge gaps pertaining to both patient experience and hypothetical scenarios. Findings were based on a systematic search, inclusion, and thematic analysis of 81 empirical peer-reviewed articles published from January 1990 to July 2019 pertaining to Latinx populations and GC/GT.

Data sharing and community-engaged research.

Data sharing must be accompanied by responsibility sharing.

Three decades of ethical, legal, and social implications research: Looking back to chart a path forward.

More than thirty years ago in the United States, the National Center for Human Genome Research (NCHGR) at the National Institutes of Health (NIH) and its partner in the Human Genome Project (HGP), the Department of Energy (DOE), called for proposals from social scientists, ethicists, lawyers, and others to explore the ethical, legal, and social implications (ELSI) of mapping and sequencing the human genome. Today, nearly twenty years after the completion of the HGP, the ELSI Research Program of the National Human Genome Research Institute (NHGRI) continues this support. It has fostered the growth of ELSI research into a global field of study, uniquely positioned at the nexus of many academic disciplines and in proximity to basic and applied scientific research.

Rising to the challenge of bias in health care AI.

Standfirst: AI-based models may amplify pre-existing human bias within datasets; addressing this problem will require fundamental a realignment of the culture of software development.

Diverse experts' perspectives on ethical issues of using machine learning to predict HIV/AIDS risk in sub-Saharan Africa: a modified Delphi study.

Objective: To better understand diverse experts' views about the ethical implications of ongoing research funded by the National Institutes of Health that uses machine learning to predict HIV/AIDS risk in sub-Saharan Africa (SSA) based on publicly available Demographic and Health Surveys data.

Design: Three rounds of semi-structured surveys in an online expert panel using a modified Delphi approach.

Participants: Experts in informatics, African public health and HIV/AIDS and bioethics were invited to participate.