Generative AI Prohibited Use Policy

Summary

Google's Generative AI Prohibited Use Policy establishes clear boundaries for acceptable use of their AI services, with a particular focus on preventing deceptive practices involving AI-generated content. What sets this policy apart is its nuanced approach to content authenticity—rather than blanket prohibitions, it specifically targets "intent to deceive" while carving out explicit exceptions for legitimate educational, creative, and research applications. This policy serves as both a protective measure for Google's platform integrity and a practical framework that other organizations can reference when developing their own AI governance policies.

The Deception vs. Disclosure Framework

At the heart of Google's policy lies a sophisticated distinction between harmful misrepresentation and legitimate AI content creation. The policy doesn't prohibit AI-generated content outright—instead, it focuses on scenarios where users deliberately present AI output as human-created with malicious intent.

Prohibited: Using generative AI to create fake testimonials, reviews, or expert opinions while claiming they're from real people to mislead consumers or manipulate decisions.

Permitted: Creating AI-generated characters for storytelling, educational simulations, or artistic projects where the AI nature is disclosed or the context makes it clear.

The policy's "benefits outweigh potential harms" test provides a practical framework for edge cases, particularly in educational and research contexts where AI-generated content serves legitimate pedagogical or scientific purposes.

Platform-Specific Enforcement Mechanisms

Google's policy goes beyond principles to outline specific enforcement actions, including content removal, account restrictions, and service limitations. The policy applies across Google's generative AI services, creating consistency whether users are working with Bard, Vertex AI, or other Google AI tools.

The enforcement approach emphasizes graduated responses—first-time violations in gray areas may result in warnings and education, while clear attempts at deception face immediate penalties. This tiered system recognizes that AI use cases exist on a spectrum of acceptability.

Who this resource is for

• Platform administrators developing content policies for AI-enabled services
• Legal teams drafting terms of service or acceptable use policies for AI products
• Educators and researchers seeking clarity on legitimate uses of AI-generated content in academic contexts
• Content creators using Google's AI tools who need to understand disclosure requirements
• Policy professionals benchmarking against industry approaches to AI content governance
• Risk and compliance teams evaluating third-party AI service agreements

What makes this different from other AI policies

Unlike many AI policies that focus on technical safety or bias prevention, Google's approach centers on user intent and transparency. The policy acknowledges that the same AI-generated output could be perfectly acceptable or clearly prohibited depending on how it's presented and used.

The explicit carve-outs for educational, documentary, scientific, and artistic purposes demonstrate a more mature understanding of AI's legitimate applications compared to earlier, more restrictive policies. This approach recognizes that blanket prohibitions on AI content would stifle innovation and legitimate use cases.

The policy's global application also means it must navigate varying cultural and legal expectations around disclosure and authenticity, making it a useful reference for organizations operating across multiple jurisdictions.

Key implementation considerations

Disclosure standards: While the policy requires avoiding deceptive misrepresentation, it doesn't specify exact disclosure language. Organizations adopting similar policies should develop clear guidelines for when and how AI use should be disclosed.

Intent assessment: Determining "intent to deceive" can be challenging at scale. Consider developing objective criteria or examples that help users self-assess and moderators make consistent decisions.

Exception criteria: The "benefits outweigh harms" test for educational and artistic use requires judgment calls. Establish clear decision-making processes and escalation paths for edge cases.

Cross-platform consistency: If you operate multiple AI services, ensure policy language and enforcement approaches align to avoid user confusion and potential loopholes.