Vision-language models like CLIP are widely used for multimodal retrieval tasks. However, they can learn historical biases from their train- ing data, resulting in the perpetuation of stereo- types and potential harm. In this study, we an- alyze the social biases present in CLIP, particu- larly in the interaction between image and text. We introduce a taxonomy of social biases called So-B-IT, consisting of 374 words categorized into ten types of bias. These biases can have nega- tive societal effects when associated with specific demographic groups. Using this taxonomy, we investigate the images retrieved by CLIP from a facial image dataset using each word as a prompt. We observe that CLIP often exhibits undesirable associations between harmful words and partic- ular demographic groups. Furthermore, we ex- plore the source of these biases by demonstrating their presence in a large image-text dataset used to train CLIP models. Our findings emphasize the significance of evaluating and mitigating bias in vision-language models, underscoring the neces- sity for transparent and fair curation of extensive pre-training datasets.