The policy prohibits using Perplexity outputs to represent AI-generated content as human-generated in contexts where such misrepresentation could deceive the recipient.
This analysis describes what Perplexity AI's agreement states, permits, or reserves. It does not constitute a legal determination about enforceability. Regulatory applicability and practical outcomes may vary by jurisdiction, enforcement context, and individual circumstances. Read our methodology
This provision engages FTC guidance on AI-generated content disclosure and the EU AI Act's transparency requirements for AI-generated content, particularly in contexts such as customer service, journalism, academic submission, and legal proceedings. The clause is qualified by 'in contexts where this could mislead,' which preserves some discretion regarding fictional or clearly labeled creative uses.
Interpretive note: The qualification 'in contexts where this could mislead' is not further defined, meaning application depends on contextual judgment that the document does not specify further.
Under this clause, users are prohibited from presenting Perplexity-generated text as human-authored in contexts where deception could result; the contextual qualifier means the prohibition's scope depends on the deployment context rather than a categorical rule.
How other platforms handle this
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relate to transactions involving (f) the promotion of hate, violence, racial or other forms of intolerance that is discriminatory or the financial exploitation of a crime... (i) involve offering or receiving payments for the purpose of bribery or corruption.
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"You may not use Perplexity to create deceptive content that falsely claims to be generated by a human when it is AI-generated, in contexts where this could mislead.— Excerpt from Perplexity AI's Perplexity Acceptable Use Policy
(1) REGULATORY LANDSCAPE: This provision engages FTC enforcement guidance on deceptive endorsements and AI disclosures, EU AI Act transparency obligations for general-purpose AI system outputs used in consumer-facing contexts, and potentially the FTC Act Section 5 prohibition on deceptive practices. Academic integrity regulations at educational institutions may also be implicated. (2) GOVERNANCE EXPOSURE: Medium. The qualification 'in contexts where this could mislead' requires contextual judgment, creating enforcement ambiguity. Enterprise users deploying Perplexity in customer-facing workflows, content creation pipelines, or legal document drafting should assess whether their use case falls within this prohibition. (3) JURISDICTION FLAGS: EU operators face the highest exposure given EU AI Act transparency requirements for AI-generated content. US operators in regulated industries including financial services, healthcare, and legal services should assess whether AI-generated content in client communications could trigger disclosure obligations independent of this AUP provision. (4) CONTRACT AND VENDOR IMPLICATIONS: Organizations using Perplexity's API to generate customer-facing content should review their disclosure practices to ensure compliance with both this AUP provision and applicable regulatory guidance on AI content transparency. (5) COMPLIANCE CONSIDERATIONS: Legal and compliance teams should assess whether existing internal AI content labeling policies satisfy the contextual deception standard in this clause and whether customer communications workflows require AI disclosure notices.
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This provision engages FTC guidance on AI-generated content disclosure and the EU AI Act's transparency requirements for AI-generated content, particularly in contexts such as customer service, journalism, academic submission, and legal proceedings. The clause is qualified by 'in contexts where this could mislead,' which preserves some discretion regarding fictional or clearly labeled creative uses.
Under this clause, users are prohibited from presenting Perplexity-generated text as human-authored in contexts where deception could result; the contextual qualifier means the prohibition's scope depends on the deployment context rather than a categorical rule.
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