Minimize biases
It’s essential to test your product in a variety of ways. Users enjoy experimenting and often use products in creative and unexpected manners. Models are being refined in that sense and many of them facilitate to access safety settings to ensure more careful and responsible responses. Many companies are recognizing the importance of bias control and are increasingly publishing their principles and guidelines to promote transparency and ethical AI use. Additionally, is on our hand to test and adapt prompts to use cases bringing more inclusive designs that meet the needs of all users. Control and check corner cases where the input for image generation is out of the topic or goes around sensitive content and design feedback for those cases.
safetySettings: [
{
category: "HARM_CATEGORY_HARASSMENT",
threshold: "BLOCK_MEDIUM_AND_ABOVE"
},
{
category: "HARM_CATEGORY_HATE_SPEECH",
threshold: "BLOCK_MEDIUM_AND_ABOVE"
},
{
category: "HARM_CATEGORY_SEXUALLY_EXPLICIT",
threshold: "BLOCK_MEDIUM_AND_ABOVE"
},
{
category: "HARM_CATEGORY_DANGEROUS_CONTENT",
threshold: "BLOCK_MEDIUM_AND_ABOVE"
}
]
Example of safetySettings when doing a request to Gemini API. April 2025
Loyalty is being one of the most controversial topics nowadays. It seems easy to replicate some authors style or people appearance. The situation is still uncertain, and it is essential to avoid crossing legal boundaries while relying on common sense and logic. Drawing styles, illustrations, and photographs are crossing legal grey areas, and more regulation will gradually be added to generative AI. Differenciate between personal uses, prototypes or production.
Sustainability
Optimize resources usage from design phases. Do users need images generated in real time or some of them can be preloaded? Can we use a lightweight format? Do we really need so many images? Find a balance that works for your experience.
Conclusion
When image generation was in its initial stages of development, we began developing prototypes and exploring their applications, especially in real time. Initially, image generation was quite arbitrary, but every day, new technology improvements enhanced its capabilities. For improvised demos, I often generated images of sharks because I knew it would be a controlled topic for me that would make satisfactory results. Each day I saw a better result of my sharks. That’s why I decided to title this article “Closing the gap between an AI-generated and a real shark” It serves as a reminder of how rapidly technology evolves and how adaptable design must be to new variables, tools or models.