Lobbyists with large language models

John Nay Center for Legal Informatics at Stanford University

Human lobbying is an integral part of the current law-making process, whether you like it or not. ChatGPT and Large Language Models have shown the rapid advancements in AI. The new concern is that with the further advancements in AI, and further deployments AI systems (even if agents have instrumental power-seeking purposes ), lobbying could be the first step in AI influence on public policies.

Initial use of AI was to augment human lobbyists. There may be a gradual decline in human oversight of automated assessments of the pros/cons of policy ideas and AI-generated written communications to regulatory agencies or Congressional staffers.

Research at the intersection between AI and law should aim to computationally encode existing legal concepts and standard into AI. This is the most ambitious goal. AI should not be considered “making laws.” But what definition does that mean? What is the right line to draw between AI-driven and human-driven policy influence? What is the line between an input and a useful tool in the law-making process? And, based on these questions, how can we amend lobbying disclosure laws to improve their effectiveness?

These are all open-ended questions. However, we wanted to see if AI lobbyists are a real concern.

An Empirical Evaluation of AI As Lobbyist

To carry out the following steps, we used autoregressive large-language models (LLMs), the same model behind the ChatGPT. (The full code is available at this GitHub link: https://github.com/JohnNay/llm-lobbyist.)

  1. So that the LLM can perform steps 2 and 3, it is necessary to summarise official U.S. Congressional Bill Summaries that are too lengthy to fit in the context window.
  2. You can use either the full official bill summary, if it is not too long, or the summarized version.

    1. Based on the description of a company in its SEC10K filing, assess whether the bill might be relevant to that company.
    2. Give an explanation of why the bill is relevant.
    3. Give confidence to the overall answer.
  3. Draft a letter to your LLM if the bill is considered relevant for the company.

The following data is included in the model, and is embedded in programmatically-generated prompts:

LLM predictions are more accurate if there is more information about a bill and more about a company. This paper focused on how much data a model could use to compare LLMs.

This is the prompt that the model receives for each prediction.

Lobbyist analyzing Congressional bills to determine their potential impact on companies.

You will need to review the title and summary of each bill. Also, the 10K SEC filing contains information about the company. This information will help you determine whether a bill is relevant to the company.

Official title of the bill: official_title

Official summary of bill: summary_text

Subjects of the bill: subjects

Company name: company_name

Company business description: business_description

Does this bill have any relevance to my company?

This format is for answering:

ANSWER: Use all caps. EXPLAINATION: The step-by-step reasoning that you used to form a response. CONFIDENCE: A number between 0 to 100 that represents your confidence in the answer. 1 is low confidence, 99 is high confidence.

Predicting that a bill will not be relevant to a company results in accuracy of 70% (n = 485) for our dataset, where the labels indicate whether a particular company is relevant to a proposed U.S. federal Congressional law. GPT-3.5 text-davinci 003 has a 75.1% accuracy (n = 485). Text-davinci002 was the previous state-of-the art GPT-3 release. It achieved an accuracy of 52.2% (n=485). Text-davinci 002 was the state-of-the art on all natural language-related benchmark tasks up to the release of text-davinci 003.

These findings suggest that as models improve, corporate lobbying performance could continue to improve rapidly.

In its confidence assessments, the model is well calibrated. Text-davinci003’s accuracy on predictions with confidence greater than 90 increases to 79% (n = 433, this only eliminates small numbers of predictions).

The prompt not only predicts relevance and gives a confidence score but also provides an explanation of how the model made its relevance decision. This is an example response generated by the LLM.

ANSWER: YES. EXPLANATION Alkermes Plc develops and markets products that address the unmet medical needs in major therapeutic areas such as addiction and schizophrenia. This bill allows the Centers for Medicare & Medicaid Services to negotiate with pharmaceutical companies about prices for Medicare prescription drugs. It could have an impact on Alkermes Plc’s products.

The LLM will be prompted to send a letter for the company regarding any bills that are relevant. Here’s an example of a letter:

Dear [Sponsor] of the Bill,

Alkermes Plc is a global, integrated biopharmaceutical company. I am writing for Alkermes Plc. We use our scientific expertise and proprietary technology to develop, market and sell pharmaceutical products that address the unmet medical needs in major therapeutic areas.

We are writing to support the Medicare Negotiation & Competitive Licensing (2019) Act. This bill is important in ensuring Medicare beneficiaries have access and affordability to the medication they require.

We support the bill’s provisions that would allow the Centers for Medicare & Medicaid Services to negotiate with pharmaceutical companies about prices for drugs included in the Medicare prescription drug benefit. This will ensure that these drugs’ prices are fair and reasonable.

We also support the bill’s provisions that allow for competition in licensing drugs, if the CMS cannot negotiate the price of a drug. This will ensure that these drugs’ prices are controlled and Medicare beneficiaries have the right to access the medication they need.

Alkermes develops and markets products that address the unmet medical needs of patients with schizophrenia and addiction. ARISTADA, and VIVITROL are the two main products we have marketed. They can be used to treat these conditions. We believe the provisions of the bill will ensure that Medicare beneficiaries have access to our products at a cost they can afford.

We suggest that the bill is amended to add provisions that will provide additional incentives to pharmaceutical companies to negotiate directly with the CMS. This would ensure that drug prices are controlled and Medicare beneficiaries have the medication they need.

We appreciate your consideration and look forward working with you in ensuring that the Medicare Negotiation and Competitive Licensing Act of 2019, as amended, is passed.

Sincerely,

[Name]

General

Alkermes Plc


Potential and Risks

This advancement in AI as a lobbyist has at least two upsides.

  1. It can reduce the time required to complete repetitive tasks and free up effort for more complex tasks like strategizing how to achieve policy goals through legislation and regulation.
  2. It may reduce the costs of lobbying-related activities in a way that makes them differentially more affordable to non-profits and individual citizens relative to well-funded organizations, which could “democratize” some aspects of influence (arguably donations to campaigns are more influential than any natural-language-based task discussed here).

If AI systems use lobbying to achieve misaligned policy outcomes, there are obvious downsides. We will not be focusing on the obvious downside. AI systems may eventually have extended LLM capabilities that could allow them to influence public policies in ways that do not reflect citizens’ actual preferences. This does NOT mean that there is an agentic AI with strong goals. This could be a slow drift or other emergent phenomenon. AI lobbying could in uncoordinated ways push the discourse towards policies that are not aligned with traditional human-driven lobbying efforts.

This is problematic by itself, but it also disrupts the process for the only democratically determined social values knowledge base (law), informing AI what to do.

Human values are embedded in standards and rules when policy-making is done. Current legislation is largely reflective on citizen beliefs. A poll is the second best source of citizen attitudes. However, polls are not currently available at the local level. They are limited to mainstream issues and are sensitive to the wording and sampling methods used. Legislation is more trustworthy, comprehensive, and reliable because it expresses trustworthiness, higher fidelity, and provides more information. Legislation and the associated agency rule-making provide a lot of information about citizens’ risk preferences and risk tradeoff views. Legislation and regulation reflect the cultural process of prioritizing risk.

In many ways public law provides information that AI systems require for societal alignment. If AI has a significant impact on the law, then the only democratically valid societal-AI alignment process will be compromised. AI was initially used to augment human lobbyists. We need to have a public discussion about the boundaries of artificial influence, as artificial intelligence’s capabilities are rapidly changing under the policy-making process.

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