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The Paperclip Mandate: When Efficiency Eats the World
The problem isn’t that AI systems lack intelligence. Reinforcement learning rewards outcomes, not wisdom. It privileges results over relevance. Russell proposes an alternative: AIs should operate under uncertainty about human preferences, continually updating their models through interaction and feedback.
Whispers to the Void
Users sometimes feed chatbots elaborate role-plays, half-truths, or outright fabrications to see what sticks, turning conversations into testing grounds for fiction. Yet when these false or misleading narratives slip into training archives, there’s no red pen to mark them “fiction”.
The Misinformation Mirror: How AI Amplifies What We Already Believe
We see a similar pattern outside of science. In education, hiring, healthcare, and media. Algorithms are constantly trained to reward engagement, not accuracy. They learn which ideas get clicks, which phrases get shared, which language feels persuasive. Over time, those patterns become predictive rules.
That is how bias becomes baseline.
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