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

• URL: http://arxiv.org/abs/2505.13182v7
• Date: Sun, 22 Jun 2025 09:45:12 GMT
• Title: Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping
• Authors: Jianfeng Xu,
• Abstract summary: This study focuses on addressing the current lack of a unified formal theoretical framework in machine learning.<n>universal definitions for model interpretability and ethical safety are proposed.<n>Three key theorems are proved: the equivalence of model interpretability and information recoverability, the assurance of ethical safety, and the estimation of generalization error.
• Score: 1.145820303039203
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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