Stop Reinventing AI Policy. Start Funding What Already Works.
Funders keep chasing the next big AI idea — while groups that enforce existing laws that could hold AI accountable are starved of support.
By Merve Hickok and Marc Rotenberg
April 15, 2026 | Read Time: 5 minutes
In just a few short years, artificial intelligence has moved from the margins of public policy to the center of debates about democracy, economic power, national security, and human rights. Yet many philanthropies still approach AI governance as if it were an emerging field, defined primarily by new ideas, pilot projects, and novel structures.
That assumption is now outdated. The central challenge in AI policy today is not how to invent new principles but how to ensure that the hard-won progress of recent years is implemented, enforced, and sustained.
What began as scattered calls for ethical AI has matured into a set of concrete governance frameworks that now shape laws, institutions, and expectations worldwide. International organizations, democratic governments, and civil society groups have converged on shared principles: transparency, accountability, fairness, due process, and human oversight.
These ideals are embedded in international laws and treaties, including the European Union’s AI Act and the Council of Europe Framework Convention on Artificial Intelligence, the first binding international treaty to align AI use with human rights, democracy, and the rule of law
In this country, states have taken the lead in passing a wide range of AI laws. The Colorado Artificial Intelligence Act, which will take effect in June, requires AI developers to protect consumers from algorithmic discrimination risks. New York’s Algorithmic Pricing Disclosure Act mandates that businesses disclose when prices are set by algorithms using personal data, and recently introduced legislation would make so-called surveillance pricing illegal altogether. In Texas, the Responsible Artificial Intelligence Governance Act prohibits discriminatory or manipulative AI uses, such as deepfakes and unauthorized biometric surveillance.
This progress reflects years of sustained investment by foundations such as the Patrick J. McGovern Foundation, the Omidyar Network, the MacArthur Foundation, and the Mozilla Foundation in building AI expertise and institutions. Overseas, the European AI and Society Fund ensures the public has a voice in regulating the industry, and in Australia, the Minderoo Foundation supports a network of academics and policy institutions working on issues such as corporate governance of AI.
As a result of these investments, dozens of countries have trained policy experts who can analyze AI systems, engage regulators, litigate when necessary, and contribute to international efforts to set AI standards. Universities in this country have launched interdisciplinary AI governance programs. Civil society organizations have developed tools to audit automated decision-making systems, assess algorithmic risk, and support communities harmed by AI.
Regulatory agencies in many states are no longer starting from scratch. They now draw on shared playbooks and peer networks developed through philanthropic support.
Yet this is precisely the moment when progress is most fragile.
Counterproductive Practices
Funders often enter a field expecting to bring about something entirely new. In many areas, that instinct is productive. In AI policy, however, it’s increasingly counterproductive. The risks include fragmentation, short-termism, and the quiet abandonment of institutions with regulatory responsibility, such as consumer protection agencies, courts, independent oversight bodies, and data protection authorities.
Too often, funding shifts away just as laws are passed, oversight bodies are formed, and courts begin to confront algorithmic decision-making in practice. For example, some early philanthropic initiatives in AI ethics have pivoted toward AI safety or technical research agendas, leaving gaps in support for implementation and accountability. The result is a familiar but dangerous pattern: ambitious frameworks on paper, paired with insufficient capacity to make them real.
Implementation of laws and standards requires sustained funding of existing institutions as enforcement agencies and courts begin applying new rules. In California, for instance, an AI transparency law was recently upheld over industry objection. The law mandates that AI developers publish summaries of their AI training data in areas such as employment, housing, and lending. As we wrote earlier in support of the measure, “Black box techniques can produce unaccountable, opaque, and often unfair results” in these high-risk areas.
Bolstering efforts like these requires funding technical expertise within regulatory agencies and independent research on the discriminatory effects of AI. Supporting litigation and legal analysis is also critical, as is funding public-interest organizations that can monitor compliance, bring complaints, and ensure civil society has a role in the rulemaking and enforcement process. Finally, grant makers can make a significant difference by backing efforts to incorporate strong global standards for AI governance into U.S. policy.
Maintaining international coordination is vital at a time when geopolitical tensions and domestic pressures are pulling democratic societies inward. AI systems are increasingly deployed in high-risk contexts — immigration, policing, welfare, credit, employment — where errors or abuses can have life-altering consequences. Without effective enforcement, even the best designed governance frameworks risk becoming paper promises.
Funding the Quieter Work
Smart philanthropy can make a decisive difference as long as it reinforces and extends what has already been built, rather than seeking to create something new. That means resisting the temptation to chase every new AI headline and instead focusing on the quieter work of funding cross-border collaboration, policy training and research centers, and organizations that translate international norms into national practice.
The window of opportunity is narrow. Public opinion polls continue to show widespread concern about the impact of AI and support for new safeguards. U.S. courts are beginning to grapple with cases in areas such as algorithmic pricing and bias in criminal sentencing algorithms. International bodies are setting precedents that will shape AI governance for decades. With sustained support, the AI policy field can advance on its gains and demonstrate that democratic oversight of advanced technology is both possible and effective.
If the moment is lost, however, much that was accomplished could be wiped away — through regulatory rollback, institutional fatigue, or the simple absence of resources to enforce the rules already on the books.
For funders, particularly those with long-standing commitments to technology, civil rights, and democracy, the message is straightforward. This is not a field in need of reinvention. It is a field in need of reinforcement — through multi-year commitments, support for enforcement capacity, and sustained investment in the institutions now responsible for democratic oversight of AI.
Whether existing AI governance policies become durable safeguards or empty promises will depend in no small part on the choices philanthropy makes now.
