Generative AI Advertising as a Problem of Trustworthy Commercial Intervention

Problem
This paper addresses a significant gap in the understanding of generative AI’s role in advertising, particularly in how it alters the dynamics of commercial influence. Existing literature primarily focuses on the placement of advertisements within content, neglecting the nuanced ways generative AI can manipulate user perceptions and behaviors through less overt interventions. The authors argue that generative AI advertising should be conceptualized as a problem of trustworthy intervention rather than mere content placement. This work is presented as a preprint and has not yet undergone peer review.

Method
The authors propose a taxonomy of generative AI advertising organized by influence tiers, which correspond to interventions on progressively more latent variables. These tiers include:

1. Product Mentions - Direct references to products.
2. Information Framing - How information is presented to shape perceptions.
3. Behavioral Redirection - Influencing user actions through subtle cues.
4. Long-term Preference Shaping - Altering user preferences over time through sustained exposure.

The paper discusses how these tiers manifest across various modalities and system architectures, including retrieval-augmented generation and agentic pipelines. The authors emphasize that current generative AI systems tend to focus on the most observable tier, which is easier to govern, while the more consequential forms of influence remain poorly understood. The paper does not disclose specific architectures, loss functions, or training compute used in their analysis.

Results
The authors do not present quantitative results or benchmark comparisons in the traditional sense, as the focus is on conceptual frameworks rather than empirical validation. They highlight that existing generative AI advertising systems primarily concentrate on observable interventions, leaving the more subtle and impactful forms of influence inadequately addressed. The implications of this focus suggest a significant oversight in current practices, which could lead to untrustworthy commercial interventions that undermine user autonomy.

Limitations
The authors acknowledge that their framework lacks empirical validation and that the mechanisms of commercial influence in generative AI systems are not yet fully understood. They also note the challenge of establishing trustworthy commercial influence, which requires interventions to be attributable, measurable, contestable, and aligned with user welfare. An obvious limitation not explicitly mentioned is the potential for the taxonomy to oversimplify the complexities of user interactions with generative AI, as individual user experiences may vary widely.

Why it matters
This work has significant implications for the design and regulation of generative AI advertising systems. By reframing the conversation around commercial influence as a problem of trustworthy intervention, it encourages researchers and practitioners to develop frameworks for detection, measurement, and disclosure of these influences. This could lead to more ethical advertising practices that prioritize user autonomy and welfare, ultimately shaping the future landscape of AI-driven marketing strategies.

Authors: Jingyi Qiu, Qiaozhu Mei
Source: arXiv:2605.18673
URL: https://arxiv.org/abs/2605.18673v1