  Existing works examining Vision Language Models (VLMs) for social biases
predominantly focus on a limited set of documented bias associations, such as
gender:profession or race:crime. This narrow scope often overlooks a vast range
of unexamined implicit associations, restricting the identification and, hence,
mitigation of such biases. We address this gap by probing VLMs to (1) uncover
hidden, implicit associations across 9 bias dimensions. We systematically
explore diverse input and output modalities and (2) demonstrate how biased
associations vary in their negativity, toxicity, and extremity. Our work (3)
identifies subtle and extreme biases that are typically not recognized by
existing methodologies. We make the Dataset of retrieved associations, (Dora),
publicly available here https://github.com/chahatraj/BiasDora.
