Mitigating Viewer Impact from Disturbing Imagery using AI Filters: A
  User-Study
Ioannis Sarridis Jochen Spangenberg Olga Papadopoulou Symeon Papadopoulos
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 (97.19%). Despite
the requirement for many professionals to eventually inspect the original
images, participants suggested potential strategies for integrating AI filters
into their workflow, such as using AI filters as an initial, preparatory step
before viewing the original image. Overall, this paper contributes to the
development of a more ethically considerate and effective visual environment
for professionals routinely engaging with potentially disturbing imagery.