Replication Data for: The Potential Impact of Emerging Technologies on Democratic Representation: Evidence from a Field Experiment
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'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 ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["audit experiment","GPT-3","representation"]} <|eot_id|>
 Here is the Description of a dataset. Description:'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 ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
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 
audit experiment; GPT-3; representation