Mitigating Viewer Impact from Disturbing Imagery using AI Filters: A User-Study
Ioannis Sarridis, Jochen Spangenberg, Olga Papadopoulou, Symeon, Papadopoulos

TL;DR
This study evaluates AI-based image filters in a user study with professionals, showing that AI Drawing filters effectively reduce emotional distress while maintaining image interpretability, offering a new approach to ethically handle disturbing imagery.
Contribution
The paper introduces and tests AI-based image filters specifically designed to mitigate emotional impact, demonstrating their effectiveness in a professional context.
Findings
AI Drawing filter reduces negative feelings by 30.38%
AI filters preserve 97.19% of image interpretability
Participants suggest integrating AI filters as a preparatory step
Abstract
Exposure to disturbing imagery can significantly impact individuals, especially professionals who encounter such content as part of their work. This paper presents a user study, involving 107 participants, predominantly journalists and human rights investigators, that explores the capability of Artificial Intelligence (AI)-based image filters to potentially mitigate the emotional impact of viewing such disturbing content. We tested five different filter styles, both traditional (Blurring and Partial Blurring) and AI-based (Drawing, Colored Drawing, and Painting), and measured their effectiveness in terms of conveying image information while reducing emotional distress. Our findings suggest that the AI-based Drawing style filter demonstrates the best performance, offering a promising solution for reducing negative feelings (-30.38%) while preserving the interpretability of the image…
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Taxonomy
TopicsArtificial Intelligence in Healthcare and Education
