How AI could write our laws

How AI could write our laws

Second, we must strengthen disclosure requirements on lobbyists, whether they are fully human or assisted by AI. State laws regarding lobbying disclosure are a hodgepodge. North Dakota, for example, only in need lobbying reports to be filed annually, so that by the time a disclosure is made, policy has likely already been decided. A lobbying disclosure scorecard created by Open Secrets, a group that researches the influence of money in US politics, tracks nine states that do not even require lobbyists to report their compensation.

Ideally, it’s good for the public to see all communications between lobbyists and lawmakers, whether it’s in the form of a proposed amendment or not. Without that, let’s give the public the benefit of reviewing something lobbyists are lobbying for—and why. Lobbying is traditionally an activity that happens behind closed doors. Today, many states are adopting that: they really are exempt testimony delivered publicly to a legislature from reporting as lobbying.

In those jurisdictions, if you disclose your position publicly, you are no longer lobbying. Let’s do the opposite: ask lobbyists to reveal their positions on issues. How many jurisdictions? in need a position statement (a ‘yes’ or ‘no’) from registered lobbyists. And for the most part (but Not all) states, you can make a public records request about meetings held with a state legislator and hope to restore something important. But we can expect more—lobbyists may be required to actively publish, within days, a brief summary of what they are asking policymakers at meetings and why they believe it is for in the general interest.

We cannot count on corporations to be forthcoming and completely honest about the reasons behind their lobbying positions. But having them on the record about their intentions would at least provide a baseline for accountability.

Finally, consider the role AI assistive technologies may play in lobbying firms themselves and the labor market for lobbyists. Many observers deserve it worried about the possibility of AI replacing or devaluing the human labor it automates. If the automation potential of AI ends up commodifying the work of political strategizing and message-building, it could indeed put some K Street professionals out of a job.

But don’t expect it to disrupt most people’s careers astronomically compensated lobbyists: former members of Congress and other insiders who pass through revolving door. Nothing short of reform ideas for limiting the ability of government officials turned lobbyists to sell access to their colleagues who are still in government, and they should be welcomed and—equally important—maintained and enforced in successive Congresses and administrations.

None of these solutions are truly original, specific to the threats posed by AI, or even more focused on microlegislation—and that’s the point. Good governance must and can be resilient to threats from a variety of methods and actors.

But what makes the risks posed by AI especially pressing today is how quickly the field is advancing. We expect the size, techniques, and effectiveness of people engaged in lobbying to evolve over the years and decades. Advances in AI, meanwhile, seem to be making impressive strides at an even faster pace—and it’s still accelerating.

The legislative process is an ongoing struggle between parties trying to control the policies of our society as they are updated, rewritten, and expanded at the federal, state, and local levels. Lobbying is an important tool for balancing different interests through our system. If it is properly controlled, perhaps lobbying can support policy makers in making fair decisions on behalf of us all.

Nathan E. Sanders is a data scientist and an affiliate at the Berkman Klein Center at Harvard University. Bruce Schneier is a security technologist and a fellow and lecturer at the Harvard Kennedy School.