FairSense-AI: Responsible AI Meets Sustainability

2025-03-04

AI Summary

This work addresses the dual challenges of algorithmic bias and environmental unsustainability in AI systems. We propose FairSense-AI—the first multimodal AI framework unifying fairness governance and green computing. It synergistically integrates large language models (LLMs) and vision-language models (VLMs) to enable fine-grained, interpretable bias detection and scoring across text and image modalities. The framework incorporates an AI risk assessment module aligned with MIT/NIST standards and enhances energy efficiency via model pruning and mixed-precision computation. Its key innovation lies in unifying algorithmic fairness interventions—including automated bias mitigation recommendations—with carbon-aware computing within a single governance architecture. Empirical evaluation demonstrates significant improvements: cross-modal bias identification accuracy increases notably, inference energy consumption drops by up to 42%, and real-world deployments validate both deployability and intervention efficacy across diverse application scenarios.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Efficient ML / Green AI

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Security and privacy of machine learning and AI applications
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT

Abstract

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)

Problem

• Detects and mitigates bias in text and images
• Aligns with AI risk assessment frameworks for ethical concerns
• Optimizes energy efficiency to reduce environmental footprint

Innovation

• Multimodal framework detects and mitigates bias
• Integrates AI risk assessment for ethical concerns
• Optimizes energy efficiency via model pruning