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Research Frontiers in Computational Epistemology and Machine Learning

Investigates how artificial intelligence systems acquire, validate, and represent knowledge, addressing fundamental questions about the nature of algorithmic understanding and epistemic justification in computational contexts.

Algorithmic Justification and the Problem of Machine Opacity

Distributed Cognition Across Neural Network Architectures

Epistemic Virtues in Automated Knowledge Generation

The Hard Problem of Machine Understanding

Inductive Bias as Implicit Metaphysics in Learning Systems

Testimony and Trust in AI-Generated Knowledge Claims

Ontological Commitment in Learned Representations

Counterfactual Reasoning and Causal Inference in Black Boxes

Knowledge Extraction from Subsymbolic Pattern Recognition

Coherence and Contradiction in Emergent Machine Semantics

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