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What is Human Biases in AI Language Models about?

The document discusses how artificial intelligence and machine learning can perpetuate human biases found in language, demonstrating that standard models like GloVe can replicate these biases. It introduces new methods for evaluating bias in text, such as the Word Embedding Association Test (WEAT), and emphasizes that biases are inherent in the language data used to train AI systems. The findings have implications for AI ethics, suggesting that while bias cannot be entirely eliminated, it must be acknowledg

Author

Quem

Language

EN