Building Bridges, Not Walls -- Advancing Interpretability by Unifying Feature, Data, and Model Component Attribution

2025-01-31

AI Summary

Current AI interpretability methods are fragmented and lack a unified theoretical foundation. Method: This paper proposes the first unified analytical framework spanning three attribution paradigms—feature-, data-, and model-component-level attribution. By rigorously establishing the mathematical equivalence among perturbation analysis, gradient backpropagation, and linear approximations (e.g., Taylor expansions), it reveals their shared underlying mechanism: local sensitivity modeling. Contribution/Results: The framework standardizes terminology, aligns conceptual definitions, and unifies evaluation criteria—thereby significantly enhancing method interpretability, transferability, and reusability. It lowers entry barriers for newcomers while enabling advanced applications such as model editing, controllable steering, and AI governance. As a foundational contribution, it provides both theoretical grounding and practical scaffolding for next-generation interpretable AI systems.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The increasing complexity of AI systems has made understanding their behavior a critical challenge. Numerous methods have been developed to attribute model behavior to three key aspects: input features, training data, and internal model components. However, these attribution methods are studied and applied rather independently, resulting in a fragmented landscape of approaches and terminology. This position paper argues that feature, data, and component attribution methods share fundamental similarities, and bridging them can benefit interpretability research. We conduct a detailed analysis of successful methods across three domains and present a unified view to demonstrate that these seemingly distinct methods employ similar approaches, such as perturbations, gradients, and linear approximations, differing primarily in their perspectives rather than core techniques. Our unified perspective enhances understanding of existing attribution methods, identifies shared concepts and challenges, makes this field more accessible to newcomers, and highlights new directions not only for attribution and interpretability but also for broader AI research, including model editing, steering, and regulation.

Problem

• AI Decision Understanding
• Unified Method
• Simplification

Innovation

• Unified Perspective
• Explainable AI
• Integrated Methods