Participation in Artificial Intelligence: Toward a Tillichian Reading of AI-Produced Images
Bibliographic record
Abstract
[Figure: see text] This paper argues that Paul Tillich's theology of art is an effective approach to assessing images generated by artificial intelligence (AI). Tillich's theology of art and concept of participation demonstrate its limits and provide a helpful supplement to the dominant approach of focusing on AI creativity and consciousness, particularly through the framework of philosopher Margaret Boden. In Tillich's theology of art, there is an existential experience of being grasped into participation in the ground of being through the artwork that comes through participation in the art. In participating in the art, one also participates in that artist's contextual answer to the question of ultimate meaning. This article finds that AI-generated images, on their own, lack intentionality and desire to express participation in the spiritual presence and so do not provide this "religious style." Rather, the participation of a human artist crafting text prompts and curating the produced images is necessary along with the AI software.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".