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Xu, S., Ge, Y., & Zhang, Y. (2023). Causal Explainable AI. In S. Li, & Z. Chu (Eds.), Machine Learning for Causal Inference (pp. 137-159). Springer International Publishing.
https://doi.org/10.1007/978-3-031-35051-1_7

has been cited by the following article:

• TITLE: The Cognitive Mechanism and Limitations of Deep Learning from the Perspective of the Three-Level Cognitive Theory

  AUTHORS: Jianwei Sun

  KEYWORDS: Three-Level Cognitive Theory, Deep Learning, Epistemology, Black-Box Dilemma, Crisis of Understanding

  JOURNAL NAME: Open Journal of Philosophy, Vol.15 No.4, November 21, 2025

  ABSTRACT: Building on the Three-Level Theory of Cognition, this paper examines the architecture and foundational principles of deep learning in order to clarify its specific cognitive mechanisms and the central epistemological challenges it poses. The cognitive efficacy of deep learning derives from the synergistic interaction of three distinct levels: computational theory defines its objectives, algorithms specify the methods for achieving them, and hardware serves as the physical substrate for algorithmic implementation. Together, these levels jointly drive the advancement of deep learning. Meanwhile, the current challenges faced by deep learning, namely the lack of interpretability and theoretical understanding, also constitute the core epistemological issues in machine learning. By identifying these constraints at each hierarchical level, this paper highlights where future research must focus to deepen our understanding of deep learning’s cognitive capabilities and to resolve its foundational epistemic problems.