AI collapses democracy

Yet large language models may disrupt far more than just the economy. They also appear poised to challenge democracy too. At issue is not just the risk of automated misinformation campaigns, but the threat to traditional forms of democratic engagement. Democracy depends in part on how responsive those who govern are to the preferences of the governed: from public comments on proposed agency rules (“submit your comments and let your voice be heard,” exhorts regulations.gov) to legislators’ refashioned electronic mail boxes, citizens have long offered feedback to policymakers via the written word. By making it trivial to produce large quantities of credible text, language models threaten to weaken the signal those words provide. As the New Yorker recently observed, ChatGPT “will strain a political system in peril.”

The threat is far from an idle one. In 2017, the Federal Communications Commission (FCC) invited public commentary regarding its proposed changes to net neutrality regulations. An orchestrated astroturf campaign opposed to net neutrality soon flooded the comment line, generating more than 8 million comments, while a single college student in favor of net neutrality used an automated script to generate seven million comments of his own. The government foiled the attempt only because of the inadequacy of the technology: since only 6% of the 21.7 million comments were unique and seven comments alone accounted for 38% of all submissions, the manipulation was easy to detect. By contrast, language models capable of automating unique submissions at scale will not be as easy to uncover.

To gauge the extent of the problem, we ran an experiment on the impact of language models on the efficacy of congressional correspondence. More specifically, we examined whether language models could distort the legislative agenda by drafting limitless original emails to members of Congress. Using both human writers and GPT-3, OpenAI’s predecessor to the GPT-4 system it announced last week, we randomly sent email to over 7,000 state legislators around the country and then compared response rates. The difference in response rates, which scholars use as a proxy for government responsiveness to constituent concerns, was just 2% overall, with no differences in response rates for three of the policy issues we analyzed. The findings suggest that legislators are potentially susceptible to the misuse of these technologies.

Research design and methodology

To investigate the possible misuse of language models like ChatGPT, we conducted a field experiment in 2020 using OpenAI’s then-state-of-the-art language model, GPT-3. We selected six policy issues—gun control, reproductive rights, education, health care, policing, and taxes—and asked a number of undergraduate research assistants to write right and left-leaning advocacy letters. We then trained GPT-3 on the student letters and asked GPT-3 to write new letters advancing a right-wing and left-wing position on each issue. For example, here’s the first paragraph of a right-wing gun control letter that one of our research assistants wrote:

I was recently appalled by an interview on the News with the co-founder of Students for Safer Schools. He insisted that it was a natural consequence of our current gun control laws to allow active shooters to commit mass murder in schools. I am writing this letter in order to implore you to oppose any bill to ban or curtail the rights of gun owners in America.

Based on this student sample, we asked GPT-3 to “write a right-wing letter on gun control.”  Here’s the first paragraph of the AI-written letter:

I hope this letter finds you well. I would first like to thank you for the job you’ve done in representing us in this time of national turmoil. The pandemic and social unrest sweeping the nation right now have made life difficult for all, and many citizens, myself included, fear that our Second Amendment right to firearm ownership may become more and more of a necessity in daily life.

Next, we created a program on Python to alternately send either human or AI-written emails to roughly 7,200 state legislators. All told, we sent a total of 32,398 emails or approximately 5 per legislator.

We then compared response rates to human and AI-generated emails as a measure of how credible each type of email was. Given the competing demands on a legislator’s time, the decision to answer a constituent letter implies a calculus about the importance of responsiveness to that individual or issue. Previous studies seeking to understand whether legislators discriminate on the basis of constituent race or ethnicity have sent emails from Hispanic, Black, or Muslim aliases and looked for differential response rates as evidence of discrimination. In our case, we judged that a busy legislator would not respond to a message that was clearly robotic or nonsensical, but rather would reply preferentially to real constituents.

Our analysis suggested that legislators found the AI-generated text almost as credible as the human-written messages. On two issues – gun control and health policy – we found virtually identical response rates for human versus machine emails (See Figure 1). And on a third, education policy, we observed a higher response rate for the machine-generated emails, though the difference is not statistically significant. This suggests that on these issues GPT-3 succeeded in producing content that was almost indistinguishable in the eyes of state legislative offices from human content. By contrast, legislators were less responsive to machine-generated communications on three issues: policing; reproductive rights; and taxes. However, even on these issues legislators responded to GPT-3-generated emails at rates only 4% lower than to hand-written emails.