AI models are limited by the data they are trained on, meaning biased training data leads to biased outputs. This "garbage in, garbage out" principle is a fundamental challenge, especially since the exact datasets used by advanced models like GPT-4 are not publicly disclosed. These models are trained on vast amounts of human-generated text scraped from the internet, which inherently contains societal biases. AI
IMPACT Highlights the inherent risk of bias in AI outputs due to data collection methods, impacting trust and fairness in AI applications.
RANK_REASON The cluster discusses a known limitation of AI models based on training data bias, citing a university resource, which falls under commentary on AI ethics.
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