How AI could write our laws

How AI could write our laws

How AI could write our laws

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Second, we should strengthen disclosure requirements for lobbyists, whether fully human or AI-assisted. State laws regarding lobbying disclosure are a hodgepodge. North Dakota, for example, only requires lobbying reports to be filed annually, so by the time a disclosure is made, policy is likely already decided. A lobbying disclosure dashboard created by Open Secrets, a research group on the influence of money in American politics, tracks nine states that don’t even require lobbyists to report their compensation.


Ideally, the public should see all communications between lobbyists and legislators, whether they take the form of a proposed amendment or not. In the absence of that, let’s give the public the benefit of reviewing thislobbyists lobby for—and why. Lobbying is traditionally an activity that takes place behind closed doors. Right now, many states are reinforcing this: they are effectively exempting testimony given publicly to a legislature from being flagged as lobbying.

In these jurisdictions, if you reveal your position to the public, you are no longer lobbying. Let’s do the opposite: force lobbyists to reveal their positions on the issues. Some jurisdictions already require a position statement (a “yes” or a “no”) from registered lobbyists. And in most (but not all) states, you can make a public records request about meetings held with a state legislator and expect to get something substantial in return. But more can be expected – lobbyists could be required to proactively publish, within days, a brief summary of what they demanded of policymakers at meetings and why they think it is in the game. ‘general interest.

We cannot rely on companies to be open and entirely honest about the reasons for their lobbying positions. But having them put on record their intentions would at least provide a baseline for accountability.

Finally, consider the role that AI assistive technologies can have on lobbying firms themselves and on the job market for lobbyists. Many observers are rightly concerned about the potential for AI to replace or devalue the human labor it automates. If the automation potential of AI ends up commodifying the work of policymaking and message development, it could indeed put some K Street professionals out of work.

But don’t expect that to disrupt the careers of the astronomically highest-paid lobbyists: former congressmen and other insiders who have come through the revolving door. There is no shortage of reform ideas to limit the ability of government officials-turned-lobbyists to sell access to their colleagues still in government, and they should be embraced and, just as importantly, upheld and enforced in Congress and administrations. successive.

None of these solutions are truly original, specific to the threats posed by AI, or even primarily focused on micro-legislation – and that’s the point. Good governance should and can be resilient to threats from a variety of techniques and actors.

But what makes the risks posed by AI particularly pressing now is the speed at which the field is developing. We expect the scale, strategies and effectiveness of those engaged in lobbying to evolve over years and decades. Advances in AI, meanwhile, seem to be making impressive inroads at a much faster rate, and it’s still accelerating.

The legislative process is a constant struggle between parties trying to control the rules of our society as they are updated, rewritten and expanded at the federal, state and local levels. Lobbying is an important tool for balancing various interests in our system. If properly regulated, lobbying can perhaps help policymakers make fair decisions on behalf of all of us.

Nathan E. Sanders is a data scientist and affiliated with the Berkman Klein Center at Harvard University. Bruce Schneier is a security technologist and a Fellow and Lecturer at Harvard Kennedy School.


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