AI systems, particularly in social networks, use complex ranking systems to show content, but these systems are often not interpretable. This lack of interpretability has led to challenges for social networks, such as difficulties in explaining to users why certain content is shown. Achieving interpretability could address both long-term safety and immediate concerns. Despite the potential positive commercial applications, understanding AI language models remains challenging due to the complexity of neural networks and the scaling of data.

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