Preparing for an Era of Deepfakes and AI-Generated Ads: A Framework for Understanding Responses to Manipulated Advertising
Bibliographic record
Abstract
Traditionally, the production and distribution of advertising material has relied on human effort and analog tools. However, technological innovations have given the advertising industry digital and automatic tools that enable advertisers to automate many advertising processes and produce “synthetic ads,” or ads comprising content based on the artificial and automatic production and modification of data. The emerging practice of synthetic advertising, to date the most sophisticated form of ad manipulation, relies on various artificial intelligence (AI) techniques, such as deepfakes and generative adversarial networks (GANs), to automatically create content that depicts an unreal, albeit convincing, artificial version of reality. In this article, a general framework is constructed to better understand how consumers respond to all forms of ad manipulation. It is anticipated that this article will help explain how consumers respond to the more sophisticated forms of synthetic ads—such as deepfakes—that are emerging at an accelerating rate. To guide research in this area, a research agenda is developed focusing on three manipulated advertising areas: ad falsity, consumer response, and originality. Furthermore, the implications for theory and industry are considered.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".