When Pointing Becomes Profiling: Social Bias and Refusal in Text-Steerable Visual Encoders

Seyedarmin Azizi, Parsa Razmara, Sajjad Ghiasvand, Ardalan Aryashad, Tina Khezresmaeilzadeh

Text-steerable visual encoders condition a frozen Vision Transformer on a natural-language prompt and point at the matching image region, producing prompt-conditioned heatmaps and features. We audit the released SteerViT model and find that its steering interface turns language-driven pointing into demographic profiling. On counterfactual face mosaics and real multi-person photographs, "the person" selects a man more often than the base rate, crime words select Black people more often than occupation words do, "the man" among four women selects the Black woman 75.6% of the time on SocialCounterfactuals, and "the monkey" selects Black people. None of them is in the backbone's unsteered saliency, and the crime association is not in the frozen text encoder; the race effects grow with steering strength and enter through the last two cross-attention layers: giving the prompt to those layers alone reproduces them. Four other grounding models, CLIPSeg, Grounding-DINO, SAM 3, and Qwen2.5-VL, show no crime routing and weaker race routing. The model cannot refuse. Its patch softmax must place its mass somewhere, and its heatmap separates a groundable request ("the police officer") from one…