Executive summary: A common argument for artificial intelligence (AI) systems deceiving humans during training (“scheming”) is that there are many more possible scheming systems that perform well compared to non-scheming systems. However, this argument neglects certain selection pressures like simplicity that may make non-schemers more likely.
Key points:
The “counting argument” says there are more high-performing scheming AI systems, so absent contrary evidence, schemers should be expected. But this assumes all systems are equally likely, which may not hold given selection pressures.
Criteria like simplicity can contribute to performance, making simpler non-schemers favored, or be separate selection pressures on their own.
The argument hinges on whether entire model classes or individual systems get weighted, but it’s unclear if number of systems or overall class properties matter more.
A “hazy counting argument” gives the possibility of schemers more weight without a strict uniform probability assumption, but is unprincipled.
The relevance of the number of possible scheming vs. non-scheming systems remains unclear without clarifying selection pressures.
Understanding criteria affecting performance and inductive biases of training algorithms deserves more scrutiny regarding scheming.
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Executive summary: A common argument for artificial intelligence (AI) systems deceiving humans during training (“scheming”) is that there are many more possible scheming systems that perform well compared to non-scheming systems. However, this argument neglects certain selection pressures like simplicity that may make non-schemers more likely.
Key points:
The “counting argument” says there are more high-performing scheming AI systems, so absent contrary evidence, schemers should be expected. But this assumes all systems are equally likely, which may not hold given selection pressures.
Criteria like simplicity can contribute to performance, making simpler non-schemers favored, or be separate selection pressures on their own.
The argument hinges on whether entire model classes or individual systems get weighted, but it’s unclear if number of systems or overall class properties matter more.
A “hazy counting argument” gives the possibility of schemers more weight without a strict uniform probability assumption, but is unprincipled.
The relevance of the number of possible scheming vs. non-scheming systems remains unclear without clarifying selection pressures.
Understanding criteria affecting performance and inductive biases of training algorithms deserves more scrutiny regarding scheming.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.