This paper delves into the concept of taste as a classifier and its role in perpetuating cultural capital. Pierre Bourdieu's study on class and taste highlights how those with higher cultural capital dictate societal notions of good taste, influencing dominated classes. In the era of machine learning visual generative methods, the perpetuation of cultural capital ownership occurs through statistical processes. The evaluation of visual neural networks' aesthetic quality involves two crucial steps: filtering out low-quality images and creating test synthetic images for evaluation during training. However, these evaluations are biased, reflecting the preferences of a select group of individuals. Aesthetic evaluation data is obtained through public rating systems, but most of their users belong to a specific demographic, leading to further homogeneity in taste. Consequently, current rating systems reinforce a limited cultural capital rooted in access to technology and computational creativity. To foster diversity in neural generative systems, the paper proposes the development of new scorer systems, incorporating ratings from individuals in the Global South, those unfamiliar with generative systems or computers, and marginalized cultures. This endeavor aims to build a more inclusive aesthetic guidance and address the dominance of specific cultural capital in the field of visual culture. Finally, we also describe Edouard Glissant's concept of opacity as a valid resistance strategy for cultures which do not want to be mapped within generative artificial intelligence models. 1 Introduction Since the release of OpenAI's Dall·E and Stability.AI's StableDiffusion in 2022, the use of neural networks to generate images from text inputs has become a common task. As we'll see, these models are trained from both a combination of text-image pairs, used by the Clip models, and a massive collection of images retrieved from the Internet for training variational autoencoders. The goal of this article is to show how the choices in the selection of images that make up the training dataset, as well as the methods, leads to aesthetic biases in the generated images. 2 Societal Biases As with any system which relies on the mass extraction of information from public data, these models incorporate much of society's problems, like gender inequalities or racism. These effects are well known and have been investigated and documented elsewhere [1]. In fact, some of the corporations running large commercial services are aware of these issues and are actively fixing them [2]. These efforts consist generally of applying another bias on top of the existing ones so it won't reflect the existing distribution of data in society. A common example of this effect is the generation of a given profession. For instance, since most representations of CEOs in the underlying data are composed of white males, a model is most likely to produce white males when asked to create a picture of such executives. To avoid this effect, which hinders the representation of women in leading positions, an artificial bias is applied. The effect is not different from the use of affirmative action, materialized as, for instance, quotas for underrepresented groups in universities or employment opportunities. To be relevant and useful in society, artificial intelligent systems must aim for a fair representation of it-even if it won't reflect reality. But societal biases, notwithstanding the importance of the issue, are not the subject of this investigation. The goal here is to draw attention to the matters of taste guidance in the development of the visual generative AI models. Aesthetic issues related to taste are often overlooked in society. In the book Distinction: A Social Critique of the Judgment of Taste, French sociologist Pierre Bourdieu dedicated an extensive research to demonstrate how dominating classes may impose their own taste on the culture of their times, in detriment to the aesthetic preferences of lower classes [3]. According to his study, classes with greater cultural capital determine what consists of good taste in society. The cultural capital consists of skills, knowledge, education and skill that