Related papers: A Taxonomy of the Biases of the Images created by Generative Artificial Intelligence

A Taxonomy of the Biases of the Images created by Generative Artificial Intelligence

URL: http://arxiv.org/abs/2407.01556v1
Date: Thu, 2 May 2024 22:01:28 GMT
Title: A Taxonomy of the Biases of the Images created by Generative Artificial Intelligence
Authors: Adriana Fernández de Caleya Vázquez, Eduardo C. Garrido-Merchán,
Abstract summary: Generative artificial intelligence models show an amazing performance creating unique content automatically just by being given a prompt by the user. We analyze in detail how the generated content by these models can be strongly biased with respect to a plethora of variables. We discuss the social, political and economical implications of these biases and possible ways to mitigate them.
Score: 2.0257616108612373
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Generative artificial intelligence models show an amazing performance creating unique content automatically just by being given a prompt by the user, which is revolutionizing several fields such as marketing and design. Not only are there models whose generated output belongs to the text format but we also find models that are able to automatically generate high quality genuine images and videos given a prompt. Although the performance in image creation seems impressive, it is necessary to slowly assess the content that these models are generating, as the users are uploading massively this material on the internet. Critically, it is important to remark that generative AI are statistical models whose parameter values are estimated given algorithms that maximize the likelihood of the parameters given an image dataset. Consequently, if the image dataset is biased towards certain values for vulnerable variables such as gender or skin color, we might find that the generated content of these models can be harmful for certain groups of people. By generating this content and being uploaded into the internet by users, these biases are perpetuating harmful stereotypes for vulnerable groups, polarizing social vision about, for example, what beauty or disability is and means. In this work, we analyze in detail how the generated content by these models can be strongly biased with respect to a plethora of variables, which we organize into a new image generative AI taxonomy. We also discuss the social, political and economical implications of these biases and possible ways to mitigate them.

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