1 citations · 2 across the 14 of their papers we have counts for
16 papers
FairLens: Benchmarking Fairness in Vision-Language Models for High-Stakes Decision-Making
Vahid Reza Khazaie, Ahmed Y. Radwan, Shaina Raza
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 fairnes…
Bias in the Picture: Benchmarking VLMs with Social-Cue News Images and LLM-as-Judge Assessment
Aravind Narayanan, Vahid Reza Khazaie, Shaina Raza
Large vision-language models (VLMs) can jointly interpret images and text, but they are also prone to absorbing and reproducing harmful social stereotypes when visual cues such as…
LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation
Ananya Raval, Aravind Narayanan, Vahid Reza Khazaie +1
Large Multimodal Models (LMMs) are typically trained on vast corpora of image-text data but are often limited in linguistic coverage, leading to biased and unfair outputs across la…
HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation
Shaina Raza, Aravind Narayanan, Vahid Reza Khazaie +6
Although recent large multimodal models (LMMs) show impressive progress on vision language tasks, their alignment with human centered (HC) principles such as fairness, ethics, incl…
Advancing Medical Representation Learning Through High-Quality Data
Negin Baghbanzadeh, Adibvafa Fallahpour, Yasaman Parhizkar +8
Despite the growing scale of medical Vision-Language datasets, the impact of dataset quality on model performance remains under-explored. We introduce Open-PMC, a high-quality medi…
VLDBench Evaluating Multimodal Disinformation with Regulatory Alignment
Shaina Raza, Ashmal Vayani, Aditya Jain +8
Detecting disinformation that blends manipulated text and images has become increasingly challenging, as AI tools make synthetic content easy to generate and disseminate. While mos…