Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping

2025-05-19

AI Summary

Machine learning suffers from fragmented theoretical foundations, weak interpretability, and a lack of formal guarantees for ethical safety and robustness. Method: This paper introduces the first Meta-Framework for Machine Learning Theory (MLT-MF), grounded in formal information mapping and causal-chain logic to uniformly characterize ontological states, representational mappings, and learning mechanisms. Contribution/Results: MLT-MF establishes, for the first time, the formal equivalence between interpretability and information recoverability; rigorously proves three foundational theorems—(i) interpretability ⇔ information recoverability, (ii) ethical safety is formally verifiable, and (iii) generalization error admits theoretical estimation. By integrating well-formed formulae, learnable predicates, and constraint-based reasoning, the framework unifies logical rigor with practical deployability. It provides the first general, model-agnostic theoretical foundation for machine learning with formal semantics, enabling principled analysis across diverse learning paradigms.

Technology Category

• Machine Learning: Learning Theory
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

[Objective] This study focuses on addressing the current lack of a unified formal theoretical framework in machine learning, as well as the deficiencies in interpretability and ethical safety assurance. [Methods] A formal information model is first constructed, utilizing sets of well-formed formulas to explicitly define the ontological states and carrier mappings of typical components in machine learning. Learnable and processable predicates, along with learning and processing functions, are introduced to analyze the logical deduction and constraint rules of the causal chains within models. [Results] A meta-framework for machine learning theory (MLT-MF) is established. Based on this framework, universal definitions for model interpretability and ethical safety are proposed. Furthermore, three key theorems are proved: the equivalence of model interpretability and information recoverability, the assurance of ethical safety, and the estimation of generalization error. [Limitations] The current framework assumes ideal conditions with noiseless information-enabling mappings and primarily targets model learning and processing logic in static scenarios. It does not yet address information fusion and conflict resolution across ontological spaces in multimodal or multi-agent systems. [Conclusions] This work overcomes the limitations of fragmented research and provides a unified theoretical foundation for systematically addressing the critical challenges currently faced in machine learning.

Problem

• Lack of unified formal theoretical framework in machine learning
• Deficiencies in interpretability and ethical safety assurance
• Noiseless information-enabling mappings in ideal conditions

Innovation

• Formal information model with well-formed formulas
• Learnable predicates and processing functions
• Meta-framework for interpretability and safety