Ethics Review of AI research: An approach to reviewing and revising existing governance structures

The recent advancements in artificial intelligence (AI), and data science more broadly, have led to a proliferation of new methods and tools, such as machine learning (ML), that are used in all kinds of scientific research, from biomedical research through to environmental and education research. Research ethics review bodies are increasingly required to review AI research protocols that cover these different fields of enquiry. Questions have been raised regarding the appropriateness of existing ethics governance principles, practices, and processes to deal with the ethical challenges that AI and data science are introducing to research. Universities and research institutions across the world are trying to understand how to translate and practically implement broad AI ethical principles into research ethics governance guidelines and processes. In this article, we report on an expert stakeholders’ workshop organised at the University of Oxford as part of the process of reviewing its ethics governance for AI research. We describe the workshop and present the reflections and recommendations that emerged from it. The aim of the article is to share the approach taken by the University of Oxford CUREC in reviewing its ethics governance processes, and the insights gained with the broader research community, as a way of contributing to this scarce body of literature, facilitating further dialogue, promoting debate and collaboration on this important issue.

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Publication Details

Journal
Research Ethics
Published
2025-12-17
DOI
https://doi.org/10.1177/17470161251406299
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00
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article

Ethics Review of AI research: An approach to reviewing and revising existing governance structures

Alexis Espinosa, Angeliki Kerasidou, Carolyn Ten Holter, Karen Melham et al.
Research Ethics
Ethics and Social Impacts of AI
article

Ethics Review of AI research: An approach to reviewing and revising existing governance structures

Alexis Espinosa, Angeliki Kerasidou, Carolyn Ten Holter, Karen Melham, Alberto Giubilini, Andrew A. S. Soltan, Michael Morrison, Andrew J. Fletcher, Christoffer Nellåker, Federica Lucivero, Jane Kaye, Patrick Thomson, Daniel O’Connor, Sara Ratner, Robert M. Klassen, Casey L Johnson, Antoniya Georgieva, Mirae Harford, Nabila Puspakesuma, P Leeson, Charalampia Kerasidou, Andrzej Ryś, John Powell, Fatima Ndanusa, Abram Schonfeldt
article en

Abstract

The recent advancements in artificial intelligence (AI), and data science more broadly, have led to a proliferation of new methods and tools, such as machine learning (ML), that are used in all kinds of scientific research, from biomedical research through to environmental and education research. Research ethics review bodies are increasingly required to review AI research protocols that cover these different fields of enquiry. Questions have been raised regarding the appropriateness of existing ethics governance principles, practices, and processes to deal with the ethical challenges that AI and data science are introducing to research. Universities and research institutions across the world are trying to understand how to translate and practically implement broad AI ethical principles into research ethics governance guidelines and processes. In this article, we report on an expert stakeholders’ workshop organised at the University of Oxford as part of the process of reviewing its ethics governance for AI research. We describe the workshop and present the reflections and recommendations that emerged from it. The aim of the article is to share the approach taken by the University of Oxford CUREC in reviewing its ethics governance processes, and the insights gained with the broader research community, as a way of contributing to this scarce body of literature, facilitating further dialogue, promoting debate and collaboration on this important issue.

Research Ethics
European Union (BE), Innovative Medicines Initiative (BE), University of Oxford (GB)
Openalex Percentile: Top 59%
Ethics and Social Impacts of AI
DOIOpen Access PDFOpenAlex
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