• IP EP10: AI Trained on Millennia of Bias, but not Allowed to be Biased

  • 2024/10/05
  • 再生時間： 14 分
  • ポッドキャスト

IP EP10: AI Trained on Millennia of Bias, but not Allowed to be Biased

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  • This episode examines the challenges of creating explainable and unbiased artificial intelligence (AI) models, particularly large language models (LLMs). The author argues that training LLMs on the entirety of human written history, which is inherently biased and unrepresentative, presents a significant challenge to ensuring fair and unbiased outputs. This is because the model's outputs will inevitably reflect the biases present in the training data. The author questions whether it is fair to demand that AI engineers "level the playing field" by forcing models to produce outputs that align with modern ideals, even if it means overcoming centuries of biased historical narratives. The text ultimately suggests that creating explainable and unbiased AI is a complex endeavor, requiring careful consideration of the inherent biases present in historical data and the ethical implications of attempting to "correct" these biases
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This episode examines the challenges of creating explainable and unbiased artificial intelligence (AI) models, particularly large language models (LLMs). The author argues that training LLMs on the entirety of human written history, which is inherently biased and unrepresentative, presents a significant challenge to ensuring fair and unbiased outputs. This is because the model's outputs will inevitably reflect the biases present in the training data. The author questions whether it is fair to demand that AI engineers "level the playing field" by forcing models to produce outputs that align with modern ideals, even if it means overcoming centuries of biased historical narratives. The text ultimately suggests that creating explainable and unbiased AI is a complex endeavor, requiring careful consideration of the inherent biases present in historical data and the ethical implications of attempting to "correct" these biases
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