Advertising in AI systems: Society must be vigilant

2025-05-23

AI Summary

This paper addresses commercial bias arising from generative AI as a novel internet gateway, formally defining the transparency challenges of “commercial AI”—characterized by dynamic, personalized, and non-attributable outputs that facilitate covert advertising and user cognitive bias. Method: Integrating stakeholder analysis, human-AI interaction design, content provenance modeling, and bias detection techniques, the study proposes cross-stakeholder (advertisers/platforms/users) collaborative governance principles and a dual-path mitigation framework emphasizing explainability enhancement and user controllability. Contribution/Results: The work delivers (1) actionable AI advertising governance principles; (2) a user-facing guide for identifying commercial bias; and (3) a prioritized list of open regulatory questions. Collectively, these contributions advance both theoretical foundations and practical paradigms for responsible AI commercialization.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Algorithmic accountability and transparency on the web
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

AI systems have increasingly become our gateways to the Internet. We argue that just as advertising has driven the monetization of web search and social media, so too will commercial incentives shape the content served by AI. Unlike traditional media, however, the outputs of these systems are dynamic, personalized, and lack clear provenance -- raising concerns for transparency and regulation. In this paper, we envision how commercial content could be delivered through generative AI-based systems. Based on the requirements of key stakeholders -- advertisers, consumers, and platforms -- we propose design principles for commercially-influenced AI systems. We then outline high-level strategies for end users to identify and mitigate commercial biases from model outputs. Finally, we conclude with open questions and a call to action towards these goals.

Problem

• Commercial incentives shaping AI content delivery
• Lack of transparency in AI-generated outputs
• Need for mitigating commercial biases in AI systems

Innovation

• Design principles for commercial AI systems
• Strategies to mitigate commercial biases
• Transparency in generative AI advertising