Using AI to write public comments improves every comment without closing the education gap

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Amelia Arsenault and Sarah Kreps asked 386 Americans to comment on the 2019 proposal to repeal the Clean Power Plan. Half got ChatGPT. The rest wrote on their own. The ones with the tool found it easier, and 43 evaluators rated their comments about 46 per cent better. They understood the rule no better than those without it.

On one measure they did improve: the share who could not describe accurately what it did fell from 31.6 to 21.2 per cent. But the gap between better- and less-educated writers held, because both improved about equally.

An agency need not weigh submissions by number, and they were never scarce: Pew found 94 per cent of the 21.7 million comments on the 2017 net-neutrality repeal were duplicates.

Agencies now use the same models to read what arrives. Sola Kim and colleagues took 182 real comments and changed only the job of the supposed author. The same words, from a street vendor rather than a financial analyst, came back summarised with more of their meaning gone. The effect is small, and differs by which company built the model, so the choice of supplier changes how much of a street vendor’s meaning survives. Federal procurement rules do not check for that.

Linked from this post:comment on the 2019 proposal to repeal the Clean Power Plan94 per cent of the 21.7 million commentschanged only the job of the supposed authorbuilt in 2018 for up to 6,000 verifications an hour and now manages 600 to 700 a dayargues that India's constitution assumed a human official would decidesplit AI in government into five kindstrained 240 civil servants as AI trainersasks5 August meeting

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