SOURCE-LINKED INTELLIGENCE

FairLens: Benchmarking Fairness in Vision-Language Models for High-Stakes Decision-Making

arXiv · AI, language, vision and robotics · article · Sep 1, 2026 · UTC

Vision-language models (VLMs) are increasingly used to make decisions from visual inputs. We introduce FAIRLENS, a benchmark and evaluation framework for measuring both the fairness and the validity of VLM responses in three high-stakes domains: hiring, legal, and healthcare. FAIRLENS pairs real face images spanning gender, race, and age groups with closed- and open-ended questions, giving more than 100K image-question pairs per model, and evaluates responses from four complementary views: demographic parity over adverse outcome rates, soundness, demographic association over unsupported roles

Read original source ↗ Open in workspace

recordType

paper

region

Global