  We demonstrate a proof-of-concept of a large language model conducting
corporate lobbying related activities. An autoregressive large language model
(OpenAI's text-davinci-003) determines if proposed U.S. Congressional bills are
relevant to specific public companies and provides explanations and confidence
levels. For the bills the model deems as relevant, the model drafts a letter to
the sponsor of the bill in an attempt to persuade the congressperson to make
changes to the proposed legislation. We use hundreds of novel ground-truth
labels of the relevance of a bill to a company to benchmark the performance of
the model. It outperforms the baseline of predicting the most common outcome of
irrelevance. We also benchmark the performance of the previous OpenAI GPT-3
model (text-davinci-002), which was the state-of-the-art model on many academic
natural language tasks until text-davinci-003 was recently released. The
performance of text-davinci-002 is worse than the simple baseline. Longer-term,
if AI begins to influence law in a manner that is not a direct extension of
human intentions, this threatens the critical role that law as information
could play in aligning AI with humans. Initially, AI is being used to simply
augment human lobbyists for a small portion of their daily tasks. However,
firms have an incentive to use less and less human oversight over automated
assessments of policy ideas and the written communication to regulatory
agencies and Congressional staffers. The core question raised is where to draw
the line between human-driven and AI-driven policy influence.
