So many slide decks and white papers promise a future of AI under human control, a project framed not as a technological sprint but as a long journey. The language is meant to reassure, a steady hand on the shoulder of a jittery public. Yet the very premise of the journey implies a certain departure, a recognition that the systems we are building now operate at a speed and complexity that have outstripped our capacity to easily oversee. One might nervously wonder whether the center will hold.

One answer to this predicament is “interpretability,” a technique for examining an AI model to figure out why it did what it did. It’s the equivalent of reading a plane’s flight recorder after a crash. But a system making thousands of autonomous decisions a second offers no time for such leisurely

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