VLBiasBench: A Comprehensive Benchmark for Evaluating Bias in Large Vision-Language Model

Large Vision-Language Models (LVLMs, which are AI systems that process both images and text) show promise for general AI but can produce biased outputs, a problem that hasn't been thoroughly studied. Researchers created VLBiasBench, a comprehensive benchmark (a standardized test for measuring performance) that evaluates nine types of social biases in these models, including age, gender, race, and disability status, using 128,342 test samples generated with images and different question formats to assess how biased 17 different LVLMs are.