AI and East Asian Philosophical and Religious Traditions: Relationality and Fluidity
Bibliographic record
Abstract
This article examines aspects of the intersection of artificial intelligence (AI) and religion, challenging Western Christian perspectives that warn against playing God and ascribing human and God-like characteristics to AI. Instead of a theistic emphasis, East Asian religious perspectives emphasize concern for the potential implications of AI on communities and relationships. This article argues for the inclusion of perspectives from Chinese and Korean traditions in the growing discourse on AI and religion to adequately address the potential social impacts of AI technologies. First, we describe some of the questions and concerns being posed regarding AI and consider how certain normative interpretations of Western Christianity may influence some of these issues. Second, we discuss the contributions of Asian philosophies and religious traditions, which emphasize relationality and fluidity, to provide alternative approaches to AI. Third, we outline the discussion of AI from Confucian, Daoist, and Buddhist traditions, which see the cosmos as an interwoven whole and both humans and the cosmos as evolving. Lastly, we introduce the example of digital resurrection (e.g., deadbots) and consider how the philosophical and theological Korean concept of Jeong might refocus our understanding of the potential impacts of this AI technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".