Imagine a government agency using artificial intelligence to screen thousands of grant applications, automatically flagging those that mention certain keywords or concepts. This isn’t a dystopian scenario – it’s happening right now at the U.S. Department of Health and Human Services. Since March 2025, HHS has deployed AI tools from Palantir to audit grants, applications, and job descriptions for compliance with executive orders targeting what the administration calls “gender ideology” and diversity, equity, and inclusion initiatives. The system represents one of the most significant government applications of AI for policy enforcement, raising fundamental questions about how algorithms should be used in public administration.
The Technical Reality Behind AI Implementation
While the HHS deployment makes headlines, it’s part of a broader trend where governments worldwide are grappling with AI’s role in regulation and enforcement. The technology promises efficiency gains – automating what would take human reviewers weeks or months – but introduces new complexities around bias, transparency, and accountability. As AI systems become more sophisticated, they’re moving beyond simple keyword matching to semantic analysis, making their decision-making processes increasingly opaque to both administrators and the public.
Global Regulatory Parallels: France’s Social Media Battle
Across the Atlantic, France is attempting a different kind of AI-adjacent regulation that highlights the challenges of national policymaking in a digital world. The French National Assembly recently approved legislation banning social media use for those under 15, a move that would require sophisticated age verification systems. However, legal experts warn this faces significant hurdles from EU regulations like the Digital Services Act, which prioritizes platform safety measures over outright bans. The conflict illustrates a fundamental tension: national governments want to protect citizens, but digital platforms operate across borders, creating regulatory gray areas.
France’s approach involves what legal scholars call “indirect regulation” – instead of directly banning platforms from serving minors, the government would declare contracts with underage users legally void, forcing companies to implement verification systems or face liability risks. This creates a fascinating parallel to the HHS case: both involve using legal and technical mechanisms to enforce policy goals, but while HHS uses AI to screen content, France seeks to use legal pressure to force AI-powered age verification.
The Broader AI Landscape: Quality Control and Economic Impact
These government applications occur against a backdrop of growing concerns about AI’s reliability and economic effects. Research from Stanford University reveals that up to 22% of computer science papers now contain AI-generated content, with 21% of reviews at the International Conference on Learning Representations being fully AI-generated in 2025. This “AI slop” – low-quality, AI-generated content – threatens to erode trust in scientific research, as noted by Hany Farid, a computer science professor at UC Berkeley: “If you’re publishing really low-quality papers that are just wrong, why should society trust us as scientists?”
Meanwhile, the economic narrative around AI is more nuanced than often portrayed. While some companies cite AI as justification for layoffs – over 50,000 in 2025 according to tracking data – employment in white-collar roles has actually increased overall since ChatGPT’s release. LinkedIn estimates AI generated 1.3 million new jobs globally between 2023 and 2025, suggesting that while AI disrupts certain roles, it also creates new opportunities. As David Deming, a labor economist at Harvard University, observes: “Over the last century, disruptive innovation has generally favoured the young and the well-educated. Today, young people’s relative tech fluency and capacity to retrain mean they can adapt to new ways of doing things.”
Security and Safety: The Unseen Risks
Behind these policy and economic discussions lie serious security concerns. Recent vulnerabilities in IBM’s Db2 database management system – including two high-risk flaws that could allow attackers to gain root access – highlight how AI systems depend on underlying infrastructure that may itself be vulnerable. When governments deploy AI for sensitive tasks like grant screening or when platforms implement AI-powered age verification, they’re building on technological foundations that require constant security maintenance.
These security concerns extend to the AI models themselves. A coalition including Public Citizen and the Center for AI and Digital Policy recently called for suspending Grok, an AI chatbot developed by Elon Musk’s xAI, from federal agencies due to concerns about generating nonconsensual sexual content and other harmful outputs. The group argues that such systems pose national security risks, especially when handling classified documents, raising questions about how governments should vet AI tools before deployment.
The Path Forward: Balancing Innovation and Oversight
What emerges from these diverse examples is a complex picture of AI’s role in governance. The HHS case shows AI being used for policy enforcement, France’s legislation demonstrates attempts to regulate digital spaces that increasingly rely on AI, security vulnerabilities reveal underlying risks, and quality concerns highlight the need for oversight even in research contexts. As Inioluwa Deborah Raji, an AI researcher at UC Berkeley, notes with irony: “There is a little bit of irony to the fact that there’s so much enthusiasm for AI shaping other fields when, in reality, our field has gone through this chaotic experience because of the widespread use of AI.”
The challenge for policymakers, technologists, and citizens is navigating this landscape without falling into either uncritical enthusiasm or reflexive opposition. AI tools can potentially make government more efficient and regulations more effective, but they require careful implementation, ongoing evaluation, and transparent oversight. As more agencies follow HHS’s lead and more countries consider France’s approach, the decisions made today will shape how AI integrates with governance for years to come – making this not just a technical discussion, but a fundamental question about how we want technology to serve society.