Brokering Ethics: A Retrospective Study on the Use of a Data Ethics Framework

Recent years have seen a surge of interest in structured tools and interventions for responsible data and technology development and deployment. As part of this phenomenon, data ethics frameworks have emerged as a basic tool that go beyond lists of `ethics principles', aiming to guide the practical application of these ethical considerations. This study offers an in-depth insight into how organisations are making use of the Data Ethics Canvas - a popular, openly licensed data ethics framework, developed by a non-profit, and used across a diverse range of organisations. Through a reflexive thematic analysis of 22 interviews with tool users, we draw key reflections to understand its role in organisational settings. We find that participants aim to use the tool for data ethics assessment, literacy, and as a foundation or inspiration for governance initiatives. We argue that the versatility of the tool offers an opportunity for `brokering' among stakeholders who approach data practices from different perspectives. Still, limited guidance and a need to overcome organisational barriers leave significant responsibility on individuals to deliver outcomes from interactions with the tool. We further discuss how far these findings can be used to reflect on wider use, re-design, re-framing and evaluation of interventions to generate responsible data practices. We offer recommendations including the need for clear and robust guidance of users' engagements with tools, and for tools to account for a landscape where responsibilities for data practices and their AI applications increasingly converge.

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Publication Details

Journal
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Published
2025-10-15
DOI
https://doi.org/10.1609/aies.v8i3.36753
Primary Topic
Data Quality and Management
Type
article
Field-Weighted Citation Impact
0.00

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article

Brokering Ethics: A Retrospective Study on the Use of a Data Ethics Framework

Elena Simperl, Georgia Panagiotidou, Sophia Worth
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Data Quality and Management
article

Brokering Ethics: A Retrospective Study on the Use of a Data Ethics Framework

Elena Simperl, Georgia Panagiotidou, Sophia Worth
article en

Abstract

Recent years have seen a surge of interest in structured tools and interventions for responsible data and technology development and deployment. As part of this phenomenon, data ethics frameworks have emerged as a basic tool that go beyond lists of `ethics principles', aiming to guide the practical application of these ethical considerations. This study offers an in-depth insight into how organisations are making use of the Data Ethics Canvas - a popular, openly licensed data ethics framework, developed by a non-profit, and used across a diverse range of organisations. Through a reflexive thematic analysis of 22 interviews with tool users, we draw key reflections to understand its role in organisational settings. We find that participants aim to use the tool for data ethics assessment, literacy, and as a foundation or inspiration for governance initiatives. We argue that the versatility of the tool offers an opportunity for `brokering' among stakeholders who approach data practices from different perspectives. Still, limited guidance and a need to overcome organisational barriers leave significant responsibility on individuals to deliver outcomes from interactions with the tool. We further discuss how far these findings can be used to reflect on wider use, re-design, re-framing and evaluation of interventions to generate responsible data practices. We offer recommendations including the need for clear and robust guidance of users' engagements with tools, and for tools to account for a landscape where responsibilities for data practices and their AI applications increasingly converge.

Proceedings of the AAAI/ACM Conference on AI Ethics and SocietyVol. 8(3)
King's College London (GB)
UK Research and Innovation
Openalex Percentile: Top 66%
Data Quality and Management
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