Abstract
This article introduces the use of AI-replicas as an alternative to traditional anonymisation methods in image-based qualitative research. It emphasises the ethical and practical dilemmas posed by current anonymisation methods, such as distortion or loss of emotional and contextual information in images, and proposes the use of AI-replicas to preserve the integrity and authenticity of visual data while ensuring participant anonymity. The article outlines the technological foundations of generative artificial intelligence (AI) and the practical application of Stable Diffusion to generate AI-replicas for anonymisation and fictionalisation purposes. Furthermore, it discusses the potential biases present in generative AI to suggest ways to mitigate these biases through careful prompt engineering and participatory approaches. The introduced approach aims to enhance ethical practices in visual research by providing a method that ensures participant anonymity without compromising the data's qualitative richness and interpretative validity.
| Original language | English |
|---|---|
| Pages (from-to) | 1300-1325 |
| Number of pages | 26 |
| Journal | Qualitative Research |
| Volume | 25 |
| Issue number | 6 |
| Early online date | 2 Jan 2025 |
| DOIs | |
| Publication status | Published - Dec 2025 |
Bibliographical note
Publisher Copyright:© The Author(s) 2025.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 9 Industry, Innovation, and Infrastructure
-
SDG 16 Peace, Justice and Strong Institutions
Keywords
- ethics
- generative AI
- image anonymisation
- privacy
- qualitative research
- research methodology
- stable diffusion
- visual research
Fingerprint
Dive into the research topics of 'AI-replicas as ethical practice: introducing an alternative to traditional anonymisation techniques in image-based research'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver