Protecting the public from the tech industry’s predatory business models and the next wave of AI harms is an enormous challenge, but we have the evidence that trying to build a healthier digital culture is absolutely worth the effort.
Opaque and insufficiently tested tools are increasingly shaping student outcomes without consistent transparency, civil rights review, or technical safeguards. States and the U.S. Department of Education can address these risks using procurement and oversight tools already within their authority.
Commercial artificial intelligence tools have recently emerged that are able to produce police reports. If the resulting reports are inaccurate, incomplete or biased, or if the process leaks confidential information, this could undermine the criminal justice system and harm citizens.
Too often, affected patients, clinicians, and regulators cannot see how the system works, why a decision was made, or whether meaningful human oversight occurred.
Existing tools from other domains, such as existing robust public engagement processes in drug development, when applied to AI deployment can help strengthen public trust in these systems and enhance perceptions of their legitimacy and the decisions they produce.
With thoughtful policy action, it is still possible to build systems that are fair, transparent, and accountable, and to earn the public trust that will ultimately determine AI’s future. We hope policymakers are ready to act.
Procurement is not merely an administrative function—it is how AI enters government and the first line of defense for responsible AI in the public sector.
Responsible AI starts with who is in the data, who is at the table, whose needs shape the outcome, and who is responsible when it falls short.
Investment should instead be directed at sectors where American technology and innovation exist but the infrastructure to commercialize them domestically does not—and where the national security case is clear.
As of March 2026, there were at least nine documented U.S. wrongful arrests tied to face recognition misidentification. Errors like these are as much human as machine.
The real opportunity of AI lies not just in the tools, but in an educator workforce prepared to wield them. When done right, this investment in human infrastructure ensures AI accelerates learning outcomes for all students, closing the “digital design divide.”
Good information sources, like collections, must be available and maintained if companies are going to successfully implement the vision of AI for science expressed by their marketing and executives.