Why Every Long Islander Should Care About the AI Framework the White House Just Released

The White House released a national AI policy framework on March 20 telling Congress how to handle artificial intelligence. Seven priorities covering child safety, intellectual property, free speech, workforce training, national security, small business support, and a single federal standard to replace the patchwork of state laws being written across the country.

Those are federal priorities, but the consequences land locally. They land at the hospital where a Long Island family gets a diagnosis, at the school district where student records pass through an AI platform, and at the small business in Nassau or Suffolk that started using AI to handle customer data six months ago without asking too many questions about what happens to that data once it leaves the building.

The framework gets the goals right. What it leaves out is the part that makes any of it enforceable.

When Northwell or NYU Langone Long Island sends patient data to an AI company for clinical decision support, that data leaves the hospital's system and enters a processing environment the hospital cannot see into. The hospital cannot verify that the data stayed protected during processing. It cannot audit what happened at the moment the AI was working with a patient's records. There is a contract that says the AI company will protect the data, and there is no technical mechanism that proves the company did.

The same problem applies to every school district on Long Island using AI-powered educational technology. Student records covered under FERPA pass through commercial AI systems that the district cannot inspect during processing. Parents trust that the data is protected because the vendor says it is.

Small businesses face the same exposure without knowing it. An accounting firm using AI to process client financials, a medical practice using AI for billing, a retailer running AI-powered customer analytics: all of them send sensitive data into environments they cannot verify at the moment of processing.

The framework proposes seven pillars of protection and none of the seven include a way to check whether AI companies are actually complying.

Long Island has one of the highest concentrations of healthcare facilities, school districts, and small businesses per capita in the country. The volume of regulated data flowing through AI systems from Long Island organizations is substantial, and it grows every month as adoption accelerates. Healthcare here is not one hospital system; it is dozens of facilities, clinics, practices, and specialty centers making independent decisions about which AI platforms to use. Long Island has 124 school districts, each evaluating or already running AI tools. Nassau and Suffolk together have over 80,000 small businesses, and the ones adopting AI are doing so without any federal infrastructure to verify what happens to their data during processing.

There is a second problem that compounds the first, and most people have never heard of it.

Researchers have documented that AI systems are trained predominantly on data from Western, English-speaking, highly educated populations, roughly 12% of the world, who produce the vast majority of the content AI learns from. The result is that AI outputs carry cultural assumptions that feel normal to the people using them because those assumptions match the culture the AI was trained on. When a school district uses AI to evaluate student writing, the cultural defaults shape the evaluation. When a healthcare system uses AI for clinical support, training data biases can affect which conditions get flagged and which get missed. When a business uses AI for hiring, the defaults shape who gets selected.

Single-provider AI deployments have no way to catch this because there is no comparison point. The bias stays invisible because every output comes from the same system trained on the same kind of data, and research shows that people trust AI recommendations automatically under time pressure, which means the bias gets reinforced rather than questioned.

Multi-provider approaches, where the same question goes to several AI systems and a human compares the results, can surface these differences. That kind of infrastructure does not exist in federal law, and the White House framework does not propose it.

An open letter to the White House published this week lays out the enforcement gap in detail and points to published infrastructure designs that show the alternative is buildable. The letter does not claim to have the final answer; it argues that the country should not settle for policy without enforcement when better options exist and are ready for federal evaluation.

What Long Islanders can do right now is straightforward. Pay attention to what the 119th Congress writes into AI legislation this year, because the enforcement layer either gets built now or it does not. Ask school districts what AI platforms they use and what verification exists that student data stays protected during processing, not before and after, but during. Ask healthcare providers the same question about patient data. If you run a business using AI, ask your vendor what happens to your data at the moment of inference. The honest answer right now is that nobody can prove it.

The full open letter is published at basilpuglisi.com.