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In a new AI research paper, a team of researchers from Stanford Law School has investigated biases present in state-of-the-art large language models (LLMs), including GPT-4, focusing particularly on disparities related to race and gender. It highlights the potential harm caused by biases encoded in these models, especially when providing advice across various scenarios, such as car purchase negotiations or election outcome predictions. The paper aims to shed light on the systemic nature of biases in LLMs and propose methods to mitigate their harmful effects on marginalized communities. Current methods struggle to address biases in LLMs, particularly the ones related
The iOS vs. Android debate feels like it’s been around forever, doesn’t it? It’s not just about which logo you prefer on your phone. These two operating systems really show us different paths for…