This episode examines the societal harms of large language models (LLMs) like ChatGPT, focusing on biases resulting from uncurated training data. LLMs often amplify existing societal biases, presenting them with a sense of authority that misleads users. The episode critiques the "bigger is better" approach to LLMs, noting that larger datasets, dominated by majority perspectives (e.g., American English, male viewpoints), marginalize minority voices.Key points include the need for curated datasets, ethical data curation practices, and greater transparency from LLM developers. The episode explores the impact of biased LLMs on sectors like healthcare, code safety, journalism, and online content, warning of an "avalanche effect" where biases compound over time, making fairness and trustworthiness in AI development crucial to avoid societal harm.

https://arxiv.org/pdf/2410.13868