Artificial intelligence and medical research databases: ethical review by data access committees.

BACKGROUND: It has been argued that ethics review committees-e.g., Research Ethics Committees, Institutional Review Boards, etc.- have weaknesses in reviewing big data and artificial intelligence research. For instance, they may, due to the novelty of the area, lack the relevant expertise for judging collective risks and benefits of such research, or they may exempt it from review in instances involving de-identified data. MAIN BODY: Focusing on the example of medical research databases we highlight here ethical issues around de-identified data sharing which motivate the need for review where oversight by ethics committees is weak. Though some argue for ethics committee reform to overcome these weaknesses, it is unclear whether or when that will happen. Hence, we argue that ethical review can be done by data access committees, since they have de facto purview of big data and artificial intelligence projects, relevant technical expertise and governance knowledge, and already take on some functions of ethical review. That said, like ethics committees, they may have functional weaknesses in their review capabilities. To strengthen that function, data access committees must think clearly about the kinds of ethical expertise, both professional and lay, that they draw upon to support their work. CONCLUSION: Data access committees can undertake ethical review of medical research databases provided they enhance that review function through professional and lay ethical expertise.

Ownership of individual-level health data, data sharing, and data governance.

BACKGROUND: The ownership status of individual-level health data affects the manner in which it is used. In this paper we analyze two competing models of the ownership status of the data discussed in the literature recently: private ownership and public ownership. MAIN BODY: In this paper we describe the limitations of these two models of data ownership with respect to individual-level health data, in particular in terms of ethical principles of justice and autonomy, risk mitigation, as well as technological, economic, and conceptual issues. We argue that undifferentiated application of neither private ownership nor public ownership will allow us to resolve all the problems associated with effective, equitable, and ethical use of data. We suggest that, instead of focusing on data ownership, we should focus on the institutional and procedural aspects of data governance, such as using Data Access Committees (DACs) or equivalent managed access processes, which can balance the elements of these two ownership frameworks. CONCLUSION: Undifferentiated application of the ownership concept (private or public) is not helpful in resolving problems associated with sharing individual-level health data. DACs or equivalent managed access processes should be an integral part of data governance. They can approve or disapprove data access requests after considering the potential benefits and harms to data subjects, their communities, primary researchers, and the wider society.

Access to Biobanks: Responsibilities Within a Research Ecosystem.

The past few decades have seen rapid increases in the size and scope of biobanks, with large-scale publicly funded ventures supporting health-related research becoming the norm. As these biobanks are increasingly asked to share their data, including for example, genome-wide analyses, questions arise about how such decisions are made, including whether applicants' research aligns with the aims of the biobank. To better understand how biobanks make decisions relating to their data use, we sought the views and experiences of those involved in decision-making relating to data access at 11 large-scale publicly funded health biobanks. We were particularly interested in how potentially contentious applications were approached. Interviewees had some concerns about decisions on applications they felt their governance structures could not reach. We ask broader questions about the responsibility of those involved in biobank access decisions-those working early in the research process-when considering such issues.

Public governance of medical artificial intelligence research in the UK: an integrated multi-scale model.

There is a growing consensus among scholars, national governments, and intergovernmental organisations of the need to involve the public in decision-making around the use of artificial intelligence (AI) in society. Focusing on the UK, this paper asks how that can be achieved for medical AI research, that is, for research involving the training of AI on data from medical research databases. Public governance of medical AI research in the UK is generally achieved in three ways, namely, via lay representation on data access committees, through patient and public involvement groups, and by means of various deliberative democratic projects such as citizens' juries, citizen panels, citizen assemblies, etc.-what we collectively call "citizen forums". As we will show, each of these public involvement initiatives have complementary strengths and weaknesses for providing oversight of medical AI research. As they are currently utilized, however, they are unable to realize the full potential of their complementarity due to insufficient information transfer across them. In order to synergistically build on their contributions, we offer here a multi-scale model integrating all three. In doing so we provide a unified public governance model for medical AI research, one that, we argue, could improve the trustworthiness of big data and AI related medical research in the future.

How might the public be authentically involved in decisions about medical data sharing for artificial intelligence (AI) research? In this paper, we highlight three ways in which public views are used to improve such decisions, namely, through lay representation on data access committees, through patient and public involvement groups, and through a variety of public engagement events we call "citizen forums." Though each approach has common strengths and weaknesses, we argue that they are unable to support each other due to a lack of proper integration. We therefore propose combining them so that they work in a more coordinated way. The combined model, we argue, could be useful for improving the trustworthiness of big data and AI related medical research in the future.

Oversight of Genomic Data Sharing: What Roles for Ethics and Data Access Committees?

Discussions regarding responsible genomic data sharing often center around ethical and legal issues such as the consent, privacy, and confidentiality of individuals, families, and communities. To ensure the ethical grounds of genomic data sharing, oversight by both research ethics and Data Access Committees (DACs) across the research lifecycle is warranted. In this article, we review these oversight practices and argue that they reveal a compelling need to clarify the scope of ethical considerations by oversight bodies and to delineate core elements such as "objectionable" data uses. Ethical oversight of genomic data sharing would be considerably improved if the relevant ethical considerations by research ethics and DACs were coordinated. We therefore suggest several mechanisms to achieve greater clarification of ethical considerations by these committees, as well as greater communication and coordination between both to ensure robust and sustained ethical oversight of genomic data sharing.