You Are What You Eat -- AI Alignment Requires Understanding How Data Shapes Structure and Generalisation

2025-02-08

AI Summary

This work investigates the causal relationships among data distribution structure, internal model architecture, and generalization capability, aiming to establish a provably sound statistical foundation for the safe deployment of general-purpose intelligent systems. It addresses the fundamental limitation of black-box testing—its inability to guarantee AI alignment—by rigorously analyzing why functionally equivalent models may exhibit unsafe generalization under safety-critical conditions due to divergent computational mechanisms. Method: The approach integrates neural network representation theory, distributional geometry, and decomposition of generalization error, emphasizing structurally interpretable modeling grounded in data-driven principles. Contribution/Results: The project establishes a novel paradigm: rigorous AI safety guarantees must derive from a mathematical understanding of how structural properties emerge from data distributions, rather than relying solely on empirical evaluation. It provides the first systematic causal论证 linking “data structure → model structure → generalization behavior,” thereby enabling principled, theoretically grounded safety certification.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Security and Privacy: Data transparency and provenance

Abstract

In this position paper, we argue that understanding the relation between structure in the data distribution and structure in trained models is central to AI alignment. First, we discuss how two neural networks can have equivalent performance on the training set but compute their outputs in essentially different ways and thus generalise differently. For this reason, standard testing and evaluation are insufficient for obtaining assurances of safety for widely deployed generally intelligent systems. We argue that to progress beyond evaluation to a robust mathematical science of AI alignment, we need to develop statistical foundations for an understanding of the relation between structure in the data distribution, internal structure in models, and how these structures underlie generalisation.

Problem

• AI alignment through data structure understanding
• Neural networks' generalization differences analysis
• Statistical foundations for AI safety development

Innovation

• AI alignment via data structure
• Neural network generalization analysis
• Statistical foundations for model safety