How to Read a Generative AI Image System: Diffusion models as a techno-social entanglement
Bibliographic record
Abstract
Generative media synthesis tools have quickly gathered the public’s attention through products such as ChatGPT for text, or DALL-E 2, Midjourney and Stable Diffusion for images. However, the design of these systems is typically obscured through interfaces (buttons labelled “imagine” or “dream”) and through the misleading label of “intelligence,” with commentators likening these systems’ behaviours to human creativity and ingenuity. Until now, the functions of these systems have focused on machine learning white papers, narrowly addressing internal technical processes. As policymakers, educators and the public grapple with these black boxes, this paper offers a systems-level analysis to clarify the entanglement of these technical systems within a broader context of data collection practices, generative models, user interfaces, generated and source images, and the broader media and cultural spheres in which they circulate. Ecological impacts and human labour concerns are also acknowledged. This paper maps out a systemic analysis of generative AI using a particular AI image generation system, Stable Diffusion, intended as a model and means to provide a common language for discussing and addressing these entanglements. Revealing the structures and relationships between the “systems within AI systems” is a means to engage with ethical controversies and techno-social possibilities more thoughtfully.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".