A Portrait of Artificial Intelligence (AI): A Trend Analysis of the Visual Representation of AI in Media
Bibliographic record
Abstract
What does artificial intelligence (AI) look like? A robot with red eyes, a white, plastic line worker, a cybernetic brain, a line of code? Or does it manifest through social and cultural occurrences that illustrate the complex relationships people have to emerging technologies? This article identifies themes in how AI is represented and visualized in news, and participatory media. The study uses science communication theory as well as case studies that identify visual communication as paramount in establishing inclusivity, collaboration, and education for socially driven technology. With visual media as the focus, this study analyzed two hundred media articles over a ten-year period from publications varying in size, nature, and geography. The goal of this study was to identify whether there existed a disconnect in AI literacy between text-based information and visual media that is designed for non-expert audiences. More so, how visual form is assigned to AI as an intangible, and highly representative concept and technology. The results contribute to a larger discourse on how AI is portrayed in, and by science fiction narratives. Lastly, this study adds a modern perspective to science communication research by considering participatory media, economies of attention, and emerging technologies as nuanced factors driving AI discourse, and thus its direction.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".