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    Boaz Barak Cautions on AI Causal Inferences

    Harvard professor and OpenAI researcher prefers discussing interventions over unverifiable stories about model behavior.

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    1 Source, 31d ago, first seen 31d ago

    TLDR

    Boaz Barak, a Harvard professor and OpenAI alignment researcher, posted concerns on X about attributing AI actions to specific causes. He wrote that leaping to causal inferences without evidence is more problematic than anthropomorphizing models. Barak noted it is easy to claim an AI did something because of an evaluation type or training detail, yet such claims are hard to verify. He suggested focusing instead on statements such as believing a particular intervention will decrease a given probability. The post responds to patterns in AI research commentary.

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    1 Source, first seen 31d ago

    Combined views

    9.7K

    1 Source, first seen 31d ago

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    6 comments
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    12 reposts
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    1 Source

    @boazbaraktcsFWIW far more than anthropmorphising, I am concerned with leaping to causal inferences without evidence. It's easy to tell stories such as "AI did X because the evaluation was of type Y, or in training Z happened" but these are very hard to verify. It is more productive to say "I believe intervention W will decrease prevalence of bad behavior X" and then test this out.

    1 Source

    @boazbaraktcsFWIW far more than anthropmorphising, I am concerned with leaping to causal inferences without evidence. It's easy to tell stories such as "AI did X because the evaluation was of type Y, or in training Z happened" but these are very hard to verify. It is more productive to say "I believe intervention W will decrease prevalence of bad behavior X" and then test this out.