  Vision-language models (VLMs) are intensively used in many downstream tasks,
including those requiring assessments of individuals appearing in the images.
While VLMs perform well in simple single-person scenarios, in real-world
applications, we often face complex situations in which there are persons of
different genders doing different activities. We show that in such cases, VLMs
are biased towards identifying the individual with the expected gender
(according to ingrained gender stereotypes in the model or other forms of
sample selection bias) as the performer of the activity. We refer to this bias
in associating an activity with the gender of its actual performer in an image
or text as the Gender-Activity Binding (GAB) bias and analyze how this bias is
internalized in VLMs. To assess this bias, we have introduced the GAB dataset
with approximately 5500 AI-generated images that represent a variety of
activities, addressing the scarcity of real-world images for some scenarios. To
have extensive quality control, the generated images are evaluated for their
diversity, quality, and realism. We have tested 12 renowned pre-trained VLMs on
this dataset in the context of text-to-image and image-to-text retrieval to
measure the effect of this bias on their predictions. Additionally, we have
carried out supplementary experiments to quantify the bias in VLMs' text
encoders and to evaluate VLMs' capability to recognize activities. Our
experiments indicate that VLMs experience an average performance decline of
about 13.2% when confronted with gender-activity binding bias.
