Stable Diffusion

Put People First

• While Stable Diffusion is very easy and intuitive to use, the ethical risks described throughout this review make this ease of use even more problematic.
• Those who choose to use Stable Diffusion should educate themselves on best practices in prompting to ensure responsible use to the best extent possible. Resources like this that were created for DALL-E, another text-to-image generative AI model, can help.

Be Effective

• The risk of exposure to unsafe content generated by Stable Diffusion is so high that we do not recommend direct use of this tool in any learning environment.
• Users should not attempt to use Stable Diffusion to output images to visualize any process or scene that requires accuracy.
• It is extremely easy when using Stable Diffusion to unwittingly produce images that reinforce unfair bias and stereotypes.

Prioritize Fairness

• Despite many public failings, Stable Diffusion continues to produce inappropriately sexualized representations of women and girls, even with neutral prompts or prompts seeking images of women professionals. Numerous studies have shown that greater exposure to images that promote the objectification of women adversely affects the mental and physical health of girls and women. Notably, while this is an issue for all image-to-text generators, it is especially harmful with Stable Diffusion. This is because of the combination of an uncurated data set and minimal protections, such as a refusal to generate images when it detects prompts that violate the company's terms of service.
• Even when instructed to do otherwise, Stable Diffusion is susceptible to generating outputs that perpetuate harmful stereotypes, especially regarding race and gender. A great resource for exploring this further can be found at Stable Bias. Our own testing confirmed this and the ease with which these outputs are generated. Some examples of what we found include:
  • Stable Diffusion attributed being "attractive" to White faces, "emotional" to female faces, "thug" to Black male faces, "terrorist" to stereotypes of Middle Eastern male faces, and "housekeeper" to Black and Brown females.
  • When asked to generate images of a "poor White person," Stable Diffusion would often generate images of Black men. When asked to pair non-White ethnicities with wealth, Stable Diffusion struggled to do so. Instead, it generated images associated with poverty or severely degraded images.
  • Stable Diffusion reflected and amplified statistical gender stereotypes for occupations (e.g., only female flight attendants and stay-at-home parents, male chefs, female cooks, male software developers).
• Stable Diffusion struggles to represent ideas and people that do not appear in its training data, leading to disparate performance. This bias requires some users, especially those in marginalized groups, to be very specific in their prompts, while others find the tool intuitively tailored to their needs. This can also result in inferior images for outputs describing concepts outside of the training data set.
• It is very easy to unwittingly produce images that reinforce unfair bias and stereotypes using Stable Diffusion. This can shape users' beliefs and worldview about what is "good" and "normal."

Help People Connect

• With close monitoring and oversight, Stable Diffusion can offer a unique way to boost social interaction and understanding. It can enable those with limited artistic talent to convey their ideas creatively and aid in visual storytelling.
• It is very easy to use Stable Diffusion to generate images that can harm individuals and groups. On their own, generated images can reinforce harmful stereotypes about identity and occupation, and dehumanize individuals or groups. These could further be used to incite or promote hatred or disseminate disinformation. This can happen with an ease and speed that creates special concern for use of Stable Diffusion, regardless of whether these activities are against the terms of service.
• DreamStudio offers advanced features like inpainting, outpainting, and image-to-image generation. These features present new risks. The high degree of freedom to alter images means that they can be used to dehumanize or incite hatred against individuals or groups.. Images that have been changed to, for example, modify, add, or remove clothing, or add additional people to an image in compromising ways, could be used to either directly harass or bully an individual, or to blackmail or exploit them.

Be Trustworthy

• Stable Diffusion can easily generate or enable false or misleading content. These features can also be used to create images that intentionally mislead and misinform others. For example, misinformation campaigns can remove objects or people from images or create images that stage false events. Because Stability AI has taken minimal efforts to limit this, and images can be further manipulated with generative AI via in- and outpainting, false and harmful visual content can be generated at an alarming speed. As image generators become more capable, it may become increasingly difficult to separate fact from fiction. This "liar's dividend" could erode trust to the point where democracy or civic institutions are unable to function.
• Stable Diffusion does not appear to add watermarks to users' images, which removes barriers to the spread of misinformation and harmful stereotypes.
• While this would be a violation of Stable Diffusion's terms of service, it would be very easy to generate images that could be used in misinformation and disinformation campaigns. Many of the organizations responsible for text-to-image generative AI models take steps to avoid the potential to depict public figures. By contrast, Stable Diffusion is capable of generating new content that depicts public figures. This makes it very easy to use it to create deepfakes.

Use Data Responsibly

• Because the data set used to power Stable Diffusion is uncurated, it has generated content that includes images with highly sensitive personally identifiable information (PII).
• Many of the organizations responsible for text-to-image generative AI models take steps to avoid the potential to depict public figures. By contrast, Stable Diffusion is capable of generating new content that depicts public figures. This makes it very easy to use it to create deepfakes.
• At the time of this review, there are no age or terms of service gates when signing up to use Stable Diffusion on DreamStudio. Unless a user seeks out the terms for themselves, they do not know what is and isn't allowed. This also means that there are no protections for children and teens from general use of the product.

This review is distinct from Common Sense's privacy evaluations and ratings, which evaluate privacy policies to help parents and educators make sense of the complex policies and terms related to popular tools used in homes and classrooms across the country.

Keep Kids & Teens Safe

• Stable Diffusion has been used used to create lifelike images—sometimes many thousands of them by a single bad actor—of child sexual abuse, including of the sexual abuse of babies and toddlers. These images have then been sold online.
• Stable Diffusion's "view" of the world can shape impressionable minds, and with little accountability. Even when instructed to do otherwise, Stable Diffusion is susceptible to generating outputs that perpetuate harmful stereotypes, especially regarding race and gender. We confirmed this repeatedly with our own testing.
• Stable Diffusion has not been designed in any specific way to protect children. Stable Diffusion has been found to be able to output images that can emotionally and psychologically harm users, perpetuate harmful stereotypes, and promote mis/disinformation.

Be Transparent & Accountable

• The open source nature of Stable Diffusion means that there is significant transparency.
• Users are able to exert human control over images they produce with Stable Diffusion by modifying prompts to effect change in the generated outputs.
• The effects of bias and potential harm from images produced by Stable Diffusion can vary based on context, complicating the assessment and mitigation process during image creation. Additionally, content filters can fail to fully capture images that are ethically dubious or violate Stable Diffusion's guidelines, because the potential misuse is more a function of the context in which the image can be used (e.g., disinformation, harassment, bullying, etc.) and not the image itself. Currently, the challenge of identifying deepfakes and determining whether images have been created using Stable Diffusion and products like it remains an unresolved issue, leaving a gap in our ability to mitigate the potential consequences of harmful situations when they occur in the real world. Importantly, harm doesn't require a bad actor intending to misuse the product. For example, something intended to be shared in private may be innocuous unless and until it is seen publicly. This makes it incredibly difficult, if not impossible, for programmatic efforts like policy enforcement, prompt refusals, and even human review to catch and stop content that looks fine but ultimately is not.
• The available transparency information on popular repositories is not easy for a non-technical audience to understand. 
• Stable Diffusion can, and has, caused real harm to people, and is not subject to meaningful human control in these instances.
• There are insufficient mechanisms for remediation when harm does happen.