A Portrait of Artificial Intelligence (AI): A Trend Analysis of the Visual Representation of AI in Media
Bibliographic record
Abstract
<p>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. </p>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".