<ul data-eligibleForWebStory="true"><li>Generative AI models' widespread use has led to concerns about bias and discrimination.</li><li>The mechanisms of bias in unconditional image generation models are not fully understood.</li><li>Bias is defined as the difference between an attribute's probability in observed vs. ideal distributions.</li><li>Researchers trained unconditional image generative models and evaluated bias shifts.</li><li>Experiments showed minor shifts in attributes between training and generated distributions.</li><li>Attribute shifts were influenced by the attribute classifier used in the evaluation.</li><li>Classifier sensitivity was observed for attributes with values on a spectrum.</li><li>There is a need for improved labeling practices and scrutiny of evaluation frameworks.</li><li>Understanding the socially complex nature of attributes is crucial in bias evaluation.</li></ul>

Bias Analysis in Unconditional Image Generative Models

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