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Hauptverfasser: Sanderson, Conrad, Schleiger, Emma, Douglas, David, Kuhnert, Petra, Lu, Qinghua
Format: Preprint
Veröffentlicht: 2024
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Online-Zugang:https://arxiv.org/abs/2401.08103
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author Sanderson, Conrad
Schleiger, Emma
Douglas, David
Kuhnert, Petra
Lu, Qinghua
author_facet Sanderson, Conrad
Schleiger, Emma
Douglas, David
Kuhnert, Petra
Lu, Qinghua
contents While the operationalisation of high-level AI ethics principles into practical AI/ML systems has made progress, there is still a theory-practice gap in managing tensions between the underlying AI ethics aspects. We cover five approaches for addressing the tensions via trade-offs, ranging from rudimentary to complex. The approaches differ in the types of considered context, scope, methods for measuring contexts, and degree of justification. None of the approaches is likely to be appropriate for all organisations, systems, or applications. To address this, we propose a framework which consists of: (i) proactive identification of tensions, (ii) prioritisation and weighting of ethics aspects, (iii) justification and documentation of trade-off decisions. The proposed framework aims to facilitate the implementation of well-rounded AI/ML systems that are appropriate for potential regulatory requirements.
format Preprint
id arxiv_https___arxiv_org_abs_2401_08103
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Resolving Ethics Trade-offs in Implementing Responsible AI
Sanderson, Conrad
Schleiger, Emma
Douglas, David
Kuhnert, Petra
Lu, Qinghua
Computers and Society
Artificial Intelligence
68T01
K.4.1; I.2.m; C.4
While the operationalisation of high-level AI ethics principles into practical AI/ML systems has made progress, there is still a theory-practice gap in managing tensions between the underlying AI ethics aspects. We cover five approaches for addressing the tensions via trade-offs, ranging from rudimentary to complex. The approaches differ in the types of considered context, scope, methods for measuring contexts, and degree of justification. None of the approaches is likely to be appropriate for all organisations, systems, or applications. To address this, we propose a framework which consists of: (i) proactive identification of tensions, (ii) prioritisation and weighting of ethics aspects, (iii) justification and documentation of trade-off decisions. The proposed framework aims to facilitate the implementation of well-rounded AI/ML systems that are appropriate for potential regulatory requirements.
title Resolving Ethics Trade-offs in Implementing Responsible AI
topic Computers and Society
Artificial Intelligence
68T01
K.4.1; I.2.m; C.4
url https://arxiv.org/abs/2401.08103