A Unified Framework and Dataset for Assessing Societal Bias in Vision-Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have highlighted the existence of social biases within large vision and language models. |
| Approach: | They propose a framework for systematically evaluating gender, race, and age biases in vision-language models with respect to professions. |
| Outcome: | The proposed framework covers all supported inference modes of the recent vision-language models, including image-to-text, text-to image, and image- to-image. |
Similar Papers
VIGNETTE: Socially Grounded Bias Evaluation for Vision-Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Existing studies on VLM bias focus on portrait-style images and gender-occupation associations . existing studies ignore broader and more complex social stereotypes and their implied harm . |
| Approach: | They propose a large-scale VQA benchmark for evaluating bias in vision-language models . they use a question-answering framework that spans factuality, perception, stereotyping, and decision making . |
| Outcome: | The proposed framework examines bias in vision-language models using 30M+ images . findings reveal subtle, multifaceted, and surprising stereotypical patterns . |
BiasDora: Exploring Hidden Biased Associations in Vision-Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies on social biases focus on a limited set of documented associations, such as gender-profession or race-crime. |
| Approach: | They propose to examine hidden, implicit bias associations across 9 bias dimensions by probing VLMs to uncover hidden, unexamined associations. |
| Outcome: | The proposed methods reveal that biases vary in negativity, toxicity, and extremity. |
Multi-Modal Bias: Introducing a Framework for Stereotypical Bias Assessment beyond Gender and Race in Vision–Language Models (2023.eacl-main)
Copied to clipboard
| Challenge: | Recent advances in self-supervised training have led to a new class of pretrained vision–language models. |
| Approach: | They propose a visual and textual bias benchmark to assess bias in self-supervised multimodal models using 3,800 images and phrases from 14 population subgroups. |
| Outcome: | The proposed model shows that it favors certain groups while maintaining the accuracy of the model. |
Examining Gender and Racial Bias in Large Vision–Language Models Using a Novel Dataset of Parallel Images (2024.eacl-long)
Copied to clipboard
| Challenge: | a new wave of large vision–language models (LVLMs) incorporate images as input in addition to text . a recent study examined potential gender and racial biases in such systems based on the perceived characteristics of the people in the input images. |
| Approach: | They examine potential gender and racial biases in large vision–language models . they query a dataset of AI-generated images of people to see whether they differ . |
| Outcome: | The proposed dataset shows that the images differ in gender and race according to the perceived characteristics of the person depicted. |
Ask Me Again Differently: GRAS for Measuring Bias in Vision Language Models on Gender, Race, Age, and Skin Tone (2026.findings-eacl)
Copied to clipboard
| Challenge: | Using vision language models, we examine demographic biases in VLMs across gender, race, age, and skin tone. |
| Approach: | They propose a benchmark for uncovering demographic biases in Vision Language Models . they propose 'Gras Bias Score' to quantify bias in VLMs based on gender, race, age and skin tone . |
| Outcome: | The proposed model achieves 98, far from the unbiased ideal of 0. |
ModSCAN: Measuring Stereotypical Bias in Large Vision-Language Models from Vision and Language Modalities (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large vision-language models have been widely used but stereotypical biases are unexplored. |
| Approach: | They propose a framework to SCAN stereotypical bias within large vision-language models . they examine stereotype biases with respect to gender and race in three scenarios . |
| Outcome: | The proposed framework can reduce stereotypical biases in large vision-language models . the currently popular models show significant stereotype biase . |
Measuring Social Bias in Vision-Language Models with Face-Only Counterfactuals from Real Photos (2026.acl-long)
Copied to clipboard
| Challenge: | Vision-Language Models (VLMs) are increasingly deployed in socially consequential settings . attribution under visual confounding is a central challenge in measuring social bias . |
| Approach: | They propose a face-only counterfactual evaluation paradigm that isolates demographic effects while preserving real-image realism. |
| Outcome: | The proposed paradigm isolates demographic effects while preserving real-image realism. |
Uncovering Bias in Large Vision-Language Models at Scale with Counterfactuals (2025.naacl-long)
Copied to clipboard
| Challenge: | Large Vision-Language Models (LVLMs) have been proposed to augment LLMs with visual inputs. |
| Approach: | They propose large vision-Language Models to augment LLMs with visual inputs. |
| Outcome: | The proposed models condition generated text on both an input image and a visual prompt, enabling a variety of use cases such as visual question answering and multimodal chat. |
Evaluating Fairness in Large Vision-Language Models Across Diverse Demographic Attributes and Prompts (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large vision-language models have demonstrated strong capabilities in open-world visual understanding, but it is not clear how they address demographic biases in real life. |
| Approach: | They propose a method to assess visual fairness in LVLMs by question-answering/classification tasks. |
| Outcome: | The proposed approach improves transparency and offers a scalable solution for fairness mitigation. |
Biases Propagate in Encoder-based Vision-Language Models: A Systematic Analysis From Intrinsic Measures to Zero-shot Retrieval Outcomes (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing encoder-based vision-language models (VLMs) contain intrinsic biases that manifest in biased outputs. |
| Approach: | They propose a framework to measure intrinsic bias propagation by correlating intrinsic bias with extrinsic bias in zero-shot text-to-image and image-totext retrieval. |
| Outcome: | The proposed framework shows that larger/better-performing models exhibit greater bias propagation, raising concerns given the trend towards increasingly complex AI models. |