Saudi Cultural Missions Theses & Dissertations
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Item Restricted Autonomous Bias Detection in Text-to-Image Models: Uncovering Biases through Image-to-Text Analysis and Confusion Matrix Visualization(Saudi Digital Library, 2023-08-25) Alotaibi, Afnan; Patras, LoannisThis paper contributes to the ongoing efforts of bias detection and mitigation in AI systems, offering a foundation for developing more inclusive and equitable technologies. In the realm of AI bias detection, recent methods relying on human in the loop have exhibited limitations in scalability and reliability. Addressing biases in text- to-image models has become paramount for ensuring ethical AI practices. This paper introduces a novel approach to uncovering gender and ethnicity biases and other kinds of biases such as Stereotype, Visual, and Ambiguity based biases. This approach compares generative model input text with image-to-text (CLIP and BLIP-2)models responses and leverages the confusion matrix for detailed visualization. Ultimately, the culmination of our approach entails a textual assessment. Utilizing a stable diffusion model, this approach involves two experiments: analyzing 10,000 images generated by gender-protected captions from CelebA (experiment 1), secondly probing biases in occupation profiling via 26,000 images generated from structured input text featuring Professions and Adjectives(experiment 2). This innovative tech- nique offers the potential to effectively mitigate biases, providing robust and new autonomous bias detection and analyzing it with two different datasets. Through experiments utilizing a stable diffusion model, biases are analyzed in both gender attributes and occupation profiling, elucidating disparities through the lens of CLIP and BLIP-2 responses. Our approach successfully uncovered evident biases towards male gender dominance and a noticeable prevalence of white ethnicity, along with other biases, within the Text-to-Image (TTI) model. This robust implemen- tation strongly supports the effectiveness of our approach in uncovering such biases.13 0