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Chuman=n⋅cˉC_{\mathrm{human}} = n \cdot \bar{c}

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The unit economics matter because a dataset’s cost is close to linear in its size: Chuman=n⋅cˉC_{\mathrm{human}} = n \cdot \bar{c}. with n comparisons collected at a mean cost cˉ\bar{c} per comparison. The direct comparison published in the RLAIF paper puts numbers on both sides of a genuinely useful substitution. Lee and colleagues estimate that an AI-generated preference label produced with two inference passes of GPT-4 — used to correct for position bias, at an average of about 830 prompt tokens and 61 tokens of chain-of-thought rationale — costs about 0.06 US dollars per example, against about 0.67 US dollars per example for a human label purchased through a commercial annotation service pricing at roughly 0.11 US…

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ChumanC_{\mathrm{human}}

Symbol C_human

ChC_human is part of the quantity the equation computes from the expression on the right.

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Chuman=n⋅cˉC_{\mathrm{human}} = n \cdot \bar{c}

Equation 5 · AI Safety

What Alignment Actually Costs

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.

The unit economics matter because a dataset’s cost is close to linear in its size: Chuman=n⋅cˉC_{\mathrm{human}} = n \cdot \bar{c}. with n comparisons collected at a mean cost cˉ\bar{c} per comparison. The direct comparison published in the RLAIF paper puts numbers on both sides of a genuinely useful substitution. Lee and colleagues estimate that an AI-generated preference label produced with two inference passes of GPT-4 — used to correct for position bias, at an average of about 830 prompt tokens and 61 tokens of chain-of-thought rationale — costs about 0.06 US dollars per example, against about 0.67 US dollars per example for a human label purchased through a commercial annotation service pricing at roughly 0.11 US…

Meanings in this article

  • nn: the practical reason the labeller headcounts above are so low.
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