Ethical AI governance: Mapping a Research Ecosystem

Knight, S; Shibani, A; Vincent, N

Ethical AI governance: Mapping a Research Ecosystem

Knight, S

Shibani, A Vincent, N

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: AI and Ethics, 2024
Issue Date:: 2024

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Field	Value	Language
dc.contributor.author	Knight, S https://orcid.org/0000-0002-8709-5780
dc.contributor.author	Shibani, A
dc.contributor.author	Vincent, N
dc.date.accessioned	2024-02-14T22:07:36Z
dc.date.available	2024-02-14T22:07:36Z
dc.date.issued	2024
dc.identifier.citation	AI and Ethics, 2024
dc.identifier.issn	2730-5961
dc.identifier.issn	2730-5961
dc.identifier.uri	http://hdl.handle.net/10453/175681
dc.description.abstract	How do we assess the positive and negative impacts of research about- or research that employs artificial intelligence (AI), and how adequate are existing research governance frameworks for these ends? That concern has seen significant recent attention, with various calls for change, and a plethora of emerging guideline documents across sectors. However, it is not clear what kinds of issues are expressed in research ethics with or on AI at present, nor how resources are drawn on in this process to support the navigation of ethical issues. Research Ethics Committees (RECs) have a well-established history in ethics governance, but there have been concerns about their capacity to adequately govern AI research. However, no study to date has examined the ways that AI-related projects engage with the ethics ecosystem, or its adequacy for this context. This paper analysed a single institution’s ethics applications for research related to AI, applying a sociomaterial lens to their analysis. Our novel methodology provides an approach to understanding ethics ecosystems across institutions. Our results suggest that existing REC models can effectively support consideration of ethical issues in AI research, we thus propose that any new materials should be embedded in this existing well-established ecosystem.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	AI and Ethics
dc.relation.isbasedon	10.1007/s43681-023-00416-z
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Ethical AI governance: Mapping a Research Ecosystem
dc.type	Journal Article
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Provost
pubs.organisational-group	University of Technology Sydney/Provost/TD School
pubs.organisational-group	University of Technology Sydney/Strength - CREDS - Centre for Research on Education in a Digital Society
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
dc.date.updated	2024-02-14T22:07:35Z
pubs.publication-status	Published

Abstract:

How do we assess the positive and negative impacts of research about- or research that employs artificial intelligence (AI), and how adequate are existing research governance frameworks for these ends? That concern has seen significant recent attention, with various calls for change, and a plethora of emerging guideline documents across sectors. However, it is not clear what kinds of issues are expressed in research ethics with or on AI at present, nor how resources are drawn on in this process to support the navigation of ethical issues. Research Ethics Committees (RECs) have a well-established history in ethics governance, but there have been concerns about their capacity to adequately govern AI research. However, no study to date has examined the ways that AI-related projects engage with the ethics ecosystem, or its adequacy for this context. This paper analysed a single institution’s ethics applications for research related to AI, applying a sociomaterial lens to their analysis. Our novel methodology provides an approach to understanding ethics ecosystems across institutions. Our results suggest that existing REC models can effectively support consideration of ethical issues in AI research, we thus propose that any new materials should be embedded in this existing well-established ecosystem.

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http://hdl.handle.net/10453/175681