The Digital Production of Cultural Images: A Study on DALL-E's Perception of Cultural Diversity
Bibliographic record
Abstract
This study explores the ways in which artificial intelligence (AI) perceives and reads cultural and traditional values. Understanding how contemporary technologies such as AI, big data analytics, deep learning and natural language processing reflect cultural values is important for making sense of and understanding individual/social life. The fact that the concept of culture is a phenomenon that is shaped and continuously reproduced in historical, social and intellectual processes shows that it is not only a legacy of the past, but also plays an important role in the construction of the future. In this respect, the study of the concept poses several various challenges. The research analyses how the artificial intelligence tool DALL-E visualises different family structures around the world. It examines the extent to which DALL-E accurately, comprehensively and deeply reflects cultural diversity when asked to select countries from seven continents. The study shows that DALL-E tends to idealise and reflect cultures and family structures and fails to represent local cultural diversity fully and with the expected values. The under-representation of indigenous and minority cultural heritage is notable in the sample from Japan, Nigeria, Canada, Brazil, Italy and Australia. The research highlights that understanding the impact of AI on cultural perceptions is critical to shaping the ethical and societal dimensions of future technological applications. It is also a rich example of how technological developments have advanced intercultural communication. At a time when learning styles are changing and speed-oriented information can easily turn into disinformation, the ways in which cultural and traditional values are transmitted have the potential to lead to misunderstandings in the encoding and transmission of human values to future generations. In order to preserve traditional ties and ensure healthy intergenerational continuity, it is important to train these technologies, which are constantly learning and evolving, in the right way.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".