The New York Times Sued Perplexity for Allegedly Using Copyrighted Content and Generating False Attributions
Description
On December 5, 2025, The New York Times sued Perplexity, alleging the company used copyrighted Times articles without permission to train its AI system and generated outputs that reproduced Times content or falsely attributed fabricated information to the newspaper. The suit claims the conduct harmed the Times's business and brand. Perplexity denies wrongdoing.
Actors
Alleged Deployers
- perplexity-ai
Alleged Developers
- perplexity-ai
Harmed Parties
- writers
- the-new-york-times
- publishers
- journalistic-integrity
- epistemic-integrity
Classification
Risk Domain
Socioeconomic & EnvironmentalRisk Subdomain
6.3. Economic and cultural devaluation of human effort
MIT Risk Profile
Topics
Source
Related Incidents (Socioeconomic & Environmental)
View all →OpenAI Allegedly Did Not Alert RCMP After ChatGPT Flagged Violent Chats Before British Columbia School Shooting
Feb 2026
RealPage Algorithm Allegedly Inflates Rents and Reduces Competition in Housing Market
Sep 2016
Kronos Scheduling Algorithm Allegedly Caused Financial Issues for Starbucks Employees
Research Papers on This Topic
View all →Long-Term Trends in the Public Perception of Artificial Intelligence
Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research
Artificial Intelligence Index Report 2025
Ethical Considerations in Artificial Intelligence Courses
Mitigation Tools
Tools related to this risk domain, via shared responsible AI topics.
Open Source
View all →AIF360
Trusted-AI/AIF360
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
⭐ 2,872
fairlearn
fairlearn/fairlearn
A Python package to assess and improve fairness of machine learning models.
⭐ 2,199
responsible-ai-toolbox
microsoft/responsible-ai-toolbox
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.