Replication Data for: The Potential Impact of Emerging Technologies on Democratic Representation: Evidence from a Field Experiment

Advances in machine learning have created natural language models that can mimic human writing style and substance. Here we investigate the challenge that machine generated content such as that produced by GPT-3 presents to democratic representation by assessing the extent to which machine-generated content can pass as constituent sentiment. We conduct a field experiment in which we send both hand-written and machine-generated letters (a total of 32,398 emails) to 7,132 state legislators. We compare legislative response rates for the human versus machine-generated constituency letters to gauge whether language models can approximate and scale up inauthentic constituency voice. Legislators were only slightly less likely to respond to AI-generated content than to human-written emails; the 2% difference in response rate was statistically significant but substantively small. Qualitative evidence sheds light on the potential perils that this technology presents for democratic representation, but also suggests potential techniques that legislators might employ to guard against AI-sourced astroturfing.