Female Objectification

Female objectification, the portrayal of women as objects of visual pleasure rather than individuals, is a significant area of research focusing on how this bias manifests across various media, from literature and film to AI-generated images. Current studies utilize computational methods, including natural language processing and computer vision techniques, to quantify objectification by analyzing textual and visual data, often leveraging pre-trained models like CLIP and Stable Diffusion to identify biases in representation. These findings highlight the pervasive nature of objectification and its reinforcement by algorithms trained on biased datasets, underscoring the need for developing methods to mitigate these biases and promote more equitable representations of women in media and technology.