Computational
Epistemology

This paper proposes Computational Epistemology, a new paradigm for training large-scale AI systems grounded in spiritual and wisdom traditions. Using the Christian faith as an initial case, we outline a technical framework that integrates curated wisdom corpora (scriptures, commentaries, theology, classical moral philosophy, faith-based finance guidelines, etc.) into every stage of model development.

Key components include a specialized pretraining pipeline with epistemic data weighting (e.g., treating parables, laws, poetry, and doctrine with different weights), a wisdom-aware tokenizer and embedding scheme for metaphorical and symbolic language, and novel training objectives (such as Virtue Consistency Loss and Epistemic Humility Regularization) to align model behavior with core virtues and humility. Post-pretraining fine-tuning incorporates Reinforcement Learning from Spiritual Feedback (RLSF) and a constitutional self-refinement loop guided by spiritual principles.

We also describe inference-time mechanisms for traceability (symbolic reasoning traces, provenance trees of sources, theological grounding explanations) and moral uncertainty modeling. Finally, we generalize the framework beyond Christianity, showing how it adapts to other traditions (Jewish, Islamic, Buddhist, Indigenous, etc.) with a comparative table of canonical sources, epistemic structures, symbols, and ethics. This work establishes a foundation for a new research field and model development philosophy that bridges advanced AI with humanity’s repositories of wisdom and faith.