Large Language Models as Corporate Lobbyists

TL;DR

This paper demonstrates a proof-of-concept where a large language model automates aspects of corporate lobbying, including relevance assessment of bills and drafting persuasive letters, highlighting potential risks of AI influence on lawmaking.

Contribution

It introduces a novel application of large language models in automating lobbying activities and benchmarks their performance using a new dataset of bill relevance labels.

Findings

The model outperforms a baseline of predicting irrelevance.

text-davinci-003 surpasses previous GPT-3 models in relevance classification.

Automated lobbying could influence policy if unchecked.

Abstract

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.