Abstract
Recent advances in large vision-language models (LVLMs) have amplified concerns about fairness, yet existing evaluations remain confined to demographic attributes and often conflate fairness with refusal behavior. This paper broadens the scope of fairness by introducing a counterfactual VQA benchmark that probes the decision boundaries of closed-source LVLMs under controlled context shifts. Each image pair differs in a single visual attribute that has been validated as irrelevant to the question, enabling ground-truth-free and refusal-aware analysis of reasoning stability. Comprehensive experiments reveal that non-demographic attributes, such as environmental context or social behavior, distort LVLM decision-making more strongly than demographic ones. Moreover, instruction-based debiasing shows limited effectiveness and can even amplify these asymmetries, whereas exposure to a small number of human norm validated examples from our benchmark encourages more consistent and balanced respo