Perplexity AI Reportedly Accused in Federal Lawsuit of Purported Copyright Infringement and False Attribution of Chicago Tribune Content

The Chicago Tribune filed a federal lawsuit alleging that Perplexity AI unlawfully reproduced and paraphrased its copyrighted journalism in generative chatbot and search outputs. The complaint claims the AI system produced substitutive answers that bypassed links to the Tribune's website, diverted revenue, and at times hallucinated inaccurate information falsely attributed to the newspaper.

Risk classification

• Primary risk domain: 6 Socioeconomic & Environmental
• Primary risk subdomain: 6.3 Economic and cultural devaluation of human effort

Perplexity's unauthorized reproduction of original journalism devalues human creative effort and threatens the financial viability of the news industry by diverting traffic and ad revenue.

Additional risk subdomains

• 7.3 Lack of capability or robustness: The AI system generates inaccurate results (hallucinations) and falsely attributes them to the newspapers, demonstrating a lack of robustness.

Causal factors

• Entity: Human
• Intent: Intentional
• Timing: Post-deployment

The risk of copyright infringement stems from intentional human decisions by Perplexity's developers to scrape publisher content and design a business model that bypasses original links.

EU AI Act risk tier

• Risk tier: 3 Limited Risk

Limited Risk: The system is characterized as a chatbot and generative search engine, which falls under the transparency obligations for chatbots and AI-generated content.

AI system and alleged parties

• AI system: Perplexity AI
• AI purpose: Question Answering; Content Search
• Behaviour type: Assistant
• Alleged developer: Perplexity AI
• Alleged deployer: Perplexity AI
• Alleged harmed parties: Journalistic integrity, Journalism, Epistemic integrity, Chicago Tribune

Harm severity

Severity ratings are based on this scale.

Highest direct severity in any category: Minor. Severity is scored from Negligible to Catastrophic in each harm category, for harm the reports describe as caused directly or indirectly by the AI system.

• Physical: direct Negligible, indirect Negligible
• Infrastructure: direct Negligible, indirect Negligible
• Property: direct Negligible, indirect Negligible
• Financial: direct Negligible, indirect Minor
• Environmental: direct Negligible, indirect Negligible
• Malicious content: direct Negligible, indirect Negligible
• Differential treatment: direct Negligible, indirect Negligible
• Civil rights: direct Negligible, indirect Negligible
• Democracy: direct Negligible, indirect Negligible
• Privacy: direct Negligible, indirect Negligible
• Psychological: direct Negligible, indirect Negligible
• Epistemic: direct Minor, indirect Negligible
• Child sexual exploitation and abuse: direct Negligible, indirect Negligible

Financial

Reported: The report explicitly describes financial losses from diverted web traffic, decreased ad revenue, and lost subscription opportunities, though specific dollar amounts are not quantified.

Indirectly caused: The publishers suffer indirect financial losses through diverted subscription and advertising revenue as users bypass their websites.

Inferred additional harm: Significant financial losses from lost revenue opportunities and substantial legal fees incurred from ongoing federal litigation.

Epistemic

Reported: The report explicitly describes epistemic harm in the form of AI hallucinations and false attributions.

Directly caused: Perplexity AI generated inaccurate results and falsely attributed them to the Chicago Tribune, potentially damaging its reputation.

Inferred additional harm: Users reading these search results may be misled by false information believed to be verified by trusted news sources, eroding public trust in journalism.

People affected

• Occurrences reported: 2

Potential causes

Management

• Prioritizing Growth Over Ethics: Focused on rapid scaling and valuation over ethical data acquisition.
• Aggressive Sales Pitching: Marketed products using 'skip the links' to actively divert publisher traffic.

Technology

• Generative AI Hallucinations: AI generates fabricated info and attributes it to publishers, damaging brand.
• Substitutive Outputs: AI produces outputs identical or highly similar to original news articles.
• Link-Bypassing Chatbot: Chatbot design encourages users to skip visiting the original source website.

Data Inputs

• Unlicensed Web Scraping: System scraped millions of copyrighted stories, videos, and images.
• Aggressive Data Collection: AI models hoover up all online data to stay competitive in a cutthroat market.

Human Factors

• Dismissive Corporate Attitude: Communications head shrugged off lawsuits as typical resistance to new tech.
• User Avoidance of Source Links: Users bypass original journalism sites, preferring consolidated AI summaries.

Process and Methods

• Lack of Content Licensing: Failed to negotiate licensing or revenue sharing deals with publishers.
• No Output Verification Process: System lacks mechanisms to prevent attributing false facts to news brands.

Regulatory Environment

• Unclear Fair Use Boundaries: Legal limits of fair use for AI training and data reuse are highly ambiguous.
• Outdated Copyright Legislation: Current copyright acts do not clearly govern generative AI outputs.

Information quality

• Classification confidence: High
• Reason for confidence: The reports are detailed, from reputable news sources, and clearly outline the legal allegations, the mechanics of Perplexity's scraping, and the specific harms of copyright infringement and brand damage from hallucinations.
• Ambiguities identified: The exact scale of financial losses and the specific instances of hallucinations are not fully detailed in the text.

Major US news publishers have sued Perplexity AI for copyright infringement and trademark dilution. While the incident poses minor risks to economic security of the media sector and could cause epistemic harm through false attributions of AI hallucinations, it has negligible direct national security implications.

• Overall national security impact: Minor
• Scope: Single nation
• Primary target: United States
• Alleged perpetrator: Perplexity AI

Threat characteristics

• Imminence: Long-term. Represents an ongoing strategic and legal dispute over copyright and AI data scraping rather than an immediate crisis.
• Autonomy: Human-supervised. The AI search engine generates and synthesizes content autonomously based on user prompts, but operates under the developer's system-level guardrails.
• Novelty: Evolved capability. Represents an advancement of existing copyright disputes, moving from model training data issues to real-time search and retrieval-augmented generation output.

Impact by dimension

• Physical security: Negligible. The incident is a civil copyright and trademark dispute with no physical threats or impacts on critical infrastructure.
• Information security: Minor. AI-generated hallucinations falsely attributed to major newspapers could degrade public trust in media, but there is no evidence of coordinated foreign or domestic information operations.
• Sovereignty: Negligible. No impact on state authority, territorial control, electoral systems, or core government operations.
• Economic security: Minor. The unauthorized scraping and content reproduction threaten the business models of major US news publishers by diverting web traffic and subscription revenues.
• Societal stability: Minor. Potential for epistemic harm and erosion of public trust in journalism due to false attributions of AI hallucinations to reputable news organizations.