Murray State Theses and Dissertations

Abstract

As Artificial Intelligence (AI) technology advances, its influence on media and societal perceptions becomes increasingly significant. This dissertation explores how popular generative visual AI models represent women and the implications for racial and body type inclusivity. This study examines women’s perceptions of AI-generated representations. Two primary research questions are being explored: (1) How do AI-generated algorithms represent women in visual outputs when prompted with broad beauty-related keywords? (2) How do women perceive their representation in these AI-generated visual outputs? Through a detailed analysis of AI-generated imagery, the research uncovers implicit and explicit biases within these systems. The research also gathered insights from women about their feelings of underrepresentation and misrepresentation in AI-generated beauty imagery. This study seeks to contribute to the development of more inclusive and equitable AI systems. This study provides valuable feedback for improving AI training data by identifying specific biases in AI-generated representations. Ultimately, this study seeks to promote diversity in AI-generated content, ensuring a more accurate and inclusive representation of all individuals, and to challenge narrow beauty standards perpetuated by technology.

Year manuscript completed

2025

Year degree awarded

2025

Author's Keywords

Artificial Intelligence (AI), Generative Artificial Intelligence (GenAI), Machine Learning, Deep Learning, General Adversarial Networks (GANs), Beauty Standards

Degree Awarded

Doctor of Education

Department

Educational Studies, Leadership and Counseling

College/School

College of Education & Human Services

Dissertation Committee Chair

Teresa Clark

Co-Director of Dissertation

Randal Wilson

Committee Member

Samir Patel

Committee Member

Brian Bourke

Document Type

Dissertation

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