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.
In International conference on machine learning
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SemJudge uses a Hierarchical Semiosis Graph based on Peircean theory to evaluate deeper artistic meaning in generative art and aligns better with human judgments than prior metrics.
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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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On Semiotic-Grounded Interpretive Evaluation of Generative Art
SemJudge uses a Hierarchical Semiosis Graph based on Peircean theory to evaluate deeper artistic meaning in generative art and aligns better with human judgments than prior metrics.