An empirical audit of one web-scraped ML training dataset reveals persistent PII after sanitization, which the authors combine with legal analysis to highlight privacy risks and advocate redefining 'publicly available' data for AI training.
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Persistent non-response bias in sample-matched 2024 polls is quantified at ρ=-0.0030 for Trump voters, and a historical-data-informed correction estimator reduces RMSE from 0.13 to 0.05.
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A Common Pool of Privacy Problems: Legal and Technical Lessons from a Large-Scale Web-Scraped Machine Learning Dataset
An empirical audit of one web-scraped ML training dataset reveals persistent PII after sanitization, which the authors combine with legal analysis to highlight privacy risks and advocate redefining 'publicly available' data for AI training.
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The Persistent Non-Response Bias in a Sample-Matched Poll for the 2024 U.S. Presidential Election
Persistent non-response bias in sample-matched 2024 polls is quantified at ρ=-0.0030 for Trump voters, and a historical-data-informed correction estimator reduces RMSE from 0.13 to 0.05.