How Lawmakers Are Regulating Real-World AI in 2025
As we venture into 2025, the challenge of regulating artificial intelligence (AI) has become increasingly complex. Lawmakers find themselves navigating the delicate balance between the need for swift action and the imperative to craft effective legislation. The urgency of this dilemma was underscored in a recent panel discussion titled “What Does Real-World AI Regulation Look Like?” featuring key figures in the policy-making arena.
The Speed of Technology vs. Legislative Processes
In an era where use cases for AI such as autonomous vehicles and AI-powered teaching bots are rapidly emerging, public understanding has not kept pace. Policymakers are grappling with the question: “How do we create governance structures that work for technology that is evolving so quickly?” This sentiment was articulated by Pennsylvania state Rep. Napoleon J. Nelson, who cautioned against lawmakers “chasing headlines” and urged a deeper understanding of AI before legislating.
Instead of reactive measures based on sensationalized narratives, Nelson suggested that legislators should develop proactive frameworks that respond to the technology’s advancement. Wilmington Councilmember James Spadola echoed this, emphasizing the need for legislation that anticipates the challenges posed by AI.
Industry Self-Regulation and Safety Checks
In the absence of formal regulations, many companies are taking it upon themselves to establish guardrails to ensure safety and compliance. John Hopkins-Gillespie, director of policy at Trustible AI, highlighted the importance of having robust safety checks and quality assurance processes in place to avoid negative publicity that could arise from AI mishaps.
Real Use Cases and Policy Gaps
Throughout the discussion, panelists shared tangible examples of AI integration in their respective domains. Spadola noted the successful application of AI tools like ShotSpotter and license plate readers in enhancing public safety in Wilmington. These technologies are viewed as assets for running municipal operations more efficiently.
Conversely, Nelson pointed to a specific case in Pennsylvania regarding cyber charter schools that sought to employ AI chatbots as teachers. The applications were ultimately denied, not due to safety concerns, but because existing policies lacked the framework to adequately evaluate the technology’s effectiveness, particularly in terms of hallucination rates and the implications of inaccuracies in educational settings.
The Need for Collaborative Education
The panelists unanimously stressed the necessity for enhanced collaboration between tech companies, policymakers, and community stakeholders. Hopkins-Gillespie called for a more proactive educational approach, wherein the tech industry educates legislators about the intricacies of AI and its implications for policy.
Spadola further emphasized the importance of building relationships between constituents and their elected representatives, advocating for ongoing dialogue rather than waiting until issues arise. This proactive engagement can foster a more informed legislative process.
Conclusion: The Waiting Game of AI Regulation
As regulatory frameworks continue to evolve, the bottleneck in policy-making is identified as political rather than technical. Nelson articulated that developing sound policy doesn’t inherently take years, but is often delayed by political contention.
Despite the regulatory uncertainty, stakeholders are encouraged to embrace AI technologies. As Hopkins-Gillespie aptly stated, “Don’t fear the technology. Learn about the technology.” This forward-thinking mindset is essential as we navigate the complexities of AI regulation in an increasingly digital world.
