Student Scholar Symposium Abstracts and Posters

Document Type

Poster

Publication Date

Fall 12-5-2024

Faculty Advisor(s)

Micol Hebron

Abstract

This study is based on understanding how text-to-image generative AI platforms perpetuate biases such as racism and sexism and decoding how this bias is programmed within large language models and datasets. In this study, the results of generative AI are analyzed through the lens of affect and affect theory, as they are applied to investigate the machine learning and computer theory behind generative AI algorithms. The purpose of the study is to explain why generative AI is biased and whether this bias is generated due to current trends or to deficits and biases within the database that it draws information from. By understanding how generative AI is coded, we seek to understand whether and how generative AI is able to predict trends, even outpacing human prediction. These conversations are all correlated to the ethical implications of generative AI, and whether as we move forward with the expansion of text-to-image AI platforms, there should be mechanisms of accountability imposed for ensuring that these platforms operate in ethical and responsible ways. This research also examines whether use of AI can also further perpetuate stigmas against race and gender by either further encouraging it by its use or rather that AI is being fed such stigmas by analyzing the current trends of the social and political climate of the world. The goal of this study, however, is to understand the origins and mechanisms of bias within generative AI, particularly with regard to tropes of sexism and racism, and to create a proposal for best practices which would help encourage and implement guidelines to create more ethical and conscientious use and application of generative AI platforms.

Comments

Presented at the Fall 2024 Student Scholar Symposium at Chapman University.

Download

Share

COinS