mlsanityData to 7 Oct 2026

FairSense-AI: Responsible AI Meets Sustainability

arXiv 2503.02865 ↗First posted 4 Mar 2025cs.CLcited by 1

On the map: Multimodal LLMs / vision-language models → Social bias and fairness in VLMs

Abstract (arXiv)

In this paper, we introduce FairSense-AI: a multimodal framework designed to detect and mitigate bias in both text and images. By leveraging Large Language Models (LLMs) and Vision-Language Models (VLMs), FairSense-AI uncovers subtle forms of prejudice or stereotyping that can appear in content, providing users with bias scores, explanatory highlights, and automated recommendations for fairness enhancements. In addition, FairSense-AI integrates an AI risk assessment component that aligns with frameworks like the MIT AI Risk Repository and NIST AI Risk Management Framework, enabling structured identification of ethical and safety concerns. The platform is optimized for energy efficiency via techniques such as model pruning and mixed-precision computation, thereby reducing its environmental footprint. Through a series of case studies and applications, we demonstrate how FairSense-AI promotes responsible AI use by addressing both the social dimension of fairness and the pressing need for sustainability in large-scale AI deployments. https://vectorinstitute.github.io/FairSense-AI, https://pypi.org/project/fair-sense-ai/ (Sustainability , Responsible AI , Large Language Models , Vision Language Models , Ethical AI , Green AI)

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Social bias and fairness in VLMs, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 25 of its 43 references that are in the map. First posted on arXiv 4 Mar 2025.

Builds on

25 of 43 references.

References · 25

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  2. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,291
  3. 2017Mixed Precision Trainingcited by 1,135
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  12. 2019Principles alone cannot guarantee ethical AIcited by 134
13 more
  1. 2019Assessing Social and Intersectional Biases in Contextualized Word Representationscited by 134
  2. 2021Demystifying the Draft EU Artificial Intelligence Actcited by 82
  3. 2024Learning or Self-aligning? Rethinking Instruction Fine-tuningcited by 43
  4. 2022Dbias: Detecting biases and ensuring Fairness in news articlescited by 28
  5. 2023NBIAS: A Natural Language Processing Framework for Bias Identification in Textcited by 23
  6. 2021Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloudcited by 19
  7. 2023Greening Large Language Models of Codecited by 19
  8. 2024Green AI: Exploring Carbon Footprints, Mitigation Strategies, and Trade Offs in Large Language Model Trainingcited by 15
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  10. 2025VLDBench Evaluating Multimodal Disinformation with Regulatory Alignmentcited by 8
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  13. 2024BEADs: Bias Evaluation Across Domainscited by 3

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025When Cars Have Stereotypes: Auditing Demographic Bias in Objects from Text-to-Image Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Fairness and Bias in Multimodal AI: A Survey
  2. 2024FairCoT: Enhancing Fairness in Text-to-Image Generation via Chain of Thought Reasoning with Multimodal Large Language Models
  3. 2025Building Trustworthy Multimodal AI: A Review of Fairness, Transparency, and Ethics in Vision-Language Tasks
  4. 2026Benchmarking Bias Mitigation Toward Fairness Without Harm from Vision to LVLMs
  5. 2025FairImagen: Post-Processing for Bias Mitigation in Text-to-Image Models
  6. 2026FairLens: Benchmarking Fairness in Vision-Language Models for High-Stakes Decision-Making
  7. 2025AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic Data
  8. 2024Debiasing Text Safety Classifiers through a Fairness-Aware Ensemble