Ethical Concerns and Stagnation in AI-Generated Art/Images

AI learns from art found online by analyzing vast amounts of data, including images and their contextual information, such as style, color palettes, brushstroke techniques, and subject matter. Through deep learning algorithms, particularly neural networks, AI models are trained on these datasets to recognize patterns, replicate styles, and create new “art”. Essentially, the AI absorbs existing creative works and uses them to generate its own interpretations or entirely new pieces based on its learned understanding.

However, as more AI-generated art floods the internet, it creates a feedback loop. When AI models continue to train on an increasingly large dataset of AI-created art, there is a risk that they will start mimicking the flaws, biases, and limitations inherent in these previous works. AI art can often exhibit certain common mistakes—overuse of specific visual elements, lack of genuine innovation, or even uncanny distortions that are a byproduct of the model’s learning process. As these types of flaws accumulate over time, they may become more prominent in the works generated by newer AI systems, especially as older AI-generated art becomes the primary reference material.

This saturation could lead to a form of stagnation or deterioration in AI art production. On top of that, using artists’ work to train AI without consent is deeply unethical. Artists dedicate time, effort, and emotion to their creations, and when AI models use their art without permission, it denies them credit, control, and compensation. This exploitation not only disrespects the artist’s intellectual property but also undermines their creative autonomy. It sets a troubling precedent where artists’ contributions are commodified for profit, without regard for their rights or the ethical implications of using their work without consent.