Reliable and Responsible Foundation Models: A Comprehensive Survey

2026-02-04

AI Summary

This work presents the first systematic survey of multimodal foundation models through the lens of reliability and responsibility, addressing critical challenges such as bias, privacy leakage, hallucination, and insufficient interpretability in high-stakes domains like law and healthcare. It comprehensively examines core research dimensions—including fairness, safety, uncertainty quantification, distributional shift, alignment techniques, and AIGC detection—clarifying the current state of the art and interconnections among these areas. Through a rigorous literature analysis, the study synthesizes technical approaches for bias mitigation, privacy preservation, and synthetic content detection, and proposes the first integrated research framework dedicated to trustworthy and responsible AI. This framework offers academia and industry a clear technical roadmap and a cross-disciplinary perspective to advance the development and deployment of dependable foundation models.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Responsible Web: Fairness, accountability, transparency and ethics of web technologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Foundation models, including Large Language Models (LLMs), Multimodal Large Language Models (MLLMs), Image Generative Models (i.e, Text-to-Image Models and Image-Editing Models), and Video Generative Models, have become essential tools with broad applications across various domains such as law, medicine, education, finance, science, and beyond. As these models see increasing real-world deployment, ensuring their reliability and responsibility has become critical for academia, industry, and government. This survey addresses the reliable and responsible development of foundation models. We explore critical issues, including bias and fairness, security and privacy, uncertainty, explainability, and distribution shift. Our research also covers model limitations, such as hallucinations, as well as methods like alignment and Artificial Intelligence-Generated Content (AIGC) detection. For each area, we review the current state of the field and outline concrete future research directions. Additionally, we discuss the intersections between these areas, highlighting their connections and shared challenges. We hope our survey fosters the development of foundation models that are not only powerful but also ethical, trustworthy, reliable, and socially responsible.

Problem

• Foundation Models
• Reliability
• Responsibility
• Bias and Fairness
• AI Ethics

Innovation

• Foundation Models
• Reliability
• Responsibility
• AI Ethics
• AIGC Detection