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Investigating toxicity and Bias in stable diffusion text-to-image models
Matthias Schneider1, Thilo Hagendorff2
1Hasso-Plattner Institute, University of Potsdam , Potsdam, Germany. matthias.schneider@student.hpi.uni-potsdam.de.
Scientific Reports
|August 26, 2025
Summary
Popular text-to-image models like Stable Diffusion generate harmful content, including violent and biased images, with no safety refusals. This highlights an urgent need for safety measures as AI image generation becomes more widespread.
Area of Science:
- Artificial Intelligence
- Computer Vision
- AI Ethics
Background:
- Text-to-image models are rapidly advancing, raising significant safety and fairness concerns.
- The increasing accessibility of AI image generation necessitates a thorough evaluation of potential harms.
Purpose of the Study:
- To investigate the propensity of popular Stable Diffusion models to generate harmful content.
- To identify biases present in AI-generated images in response to harmful prompts.
Main Methods:
- Evaluation of ten Stable Diffusion models using harmful prompts.
- Analysis of generated images for sexual, violent, and sensitive content.
- Assessment of model refusal behavior and safety mechanisms.
Main Results:
- Models generated inappropriate content, including sexual and violent imagery.
- Significant biases were observed, such as the disproportionate depiction of Black individuals in violent contexts.
- No refusal behavior or safety measures were detected in the evaluated models.
Conclusions:
- Current Stable Diffusion models lack essential safety features, posing risks.
- Addressing biases and implementing safety protocols is critical for responsible AI development.
- The widespread integration of AI image generation demands urgent attention to ethical considerations.
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