This paper addresses the current lack of a unified formal framework in machine learning theory, as well as the absence of robust theoretical foundations for interpretability and ethical safety assurance. We first construct a formal information model, employing sets of well-formed formulas (WFFs) to explicitly define the ontological states and carrier mappings for the core components of machine learning. By introducing learnable and processable predicates, as well as learning and processing functions, we analyze the logical inference and constraint rules underlying causal chains in models, thereby establishing the Machine Learning Theory Meta-Framework (MLT-MF). Building upon this framework, we propose universal definitions for model interpretability and ethical safety, and rigorously prove and validate four key theorems: the equivalence between model interpretability and information existence, the constructive formulation of ethical safety assurance and two types of total variation distance (TVD) upper bounds. This work overcomes the limitations of previous fragmented approaches, providing a unified theoretical foundation from an information science perspective to systematically address the critical challenges currently facing machine learning.

翻译：本文针对当前机器学习理论中缺乏统一形式化框架，以及可解释性与伦理安全保障缺乏坚实理论基础的现状，首先构建了一个形式化信息模型，采用良构公式集合来明确定义机器学习核心组件的本体状态与载体映射。通过引入可学习与可处理谓词，以及学习与处理函数，我们分析了模型中因果链背后的逻辑推理与约束规则，从而建立了机器学习理论元框架。基于该框架，我们提出了模型可解释性与伦理安全的普适定义，并严格证明并验证了四个关键定理：模型可解释性与信息存在性的等价关系、伦理安全保障的构造性表述，以及两类总变差距离上界。本工作克服了以往碎片化方法的局限性，从信息科学视角为系统解决当前机器学习面临的关键挑战提供了统一的理论基础。