The AI Journal: When it comes to AI, we need to stop regulating tools and start regulating systems
AI regulation is one of the hottest topics in politics right now, with 1,500 AI-related state laws already introduced this year. And last month, the current U.S. administration promulgated a controversial legislative framework that simultaneously tries to minimize state regulation and advance its own policy goals. The problem is, most of these proposals are misguided; they focus on tools rather than on the systems and people that use them.
Regulators need a holistic view of AI systems to prevent harms like deepfakes without sacrificing the benefits of the technology. Fortunately, while AI is perceived as an entirely new challenge for governance, the U.S. has a deep history of oversight for algorithm-based systems to draw upon.
We have more experience with automated decision-making systems, of which AI is the latest variant, than is popularly appreciated. The IRS first used computers and algorithms to select taxpayer returns to audit in 1962. The first U.S. autonomous weapons platform, the Navy’s Aegis system, entered service in 1983. FICO’s credit score, produced via algorithm, made its debut in 1989.
When I joined the software company Lotus in 1996, it already offered automated workflow and e-commerce applications over intranets. AI differs in degree, but not in kind, from these historical predecessors. The history of algorithmic systems is not only a guiding light for governance; it is a cause for optimism. Society has used computer-driven analysis in critical decisions for decades, with occasional setbacks but considerable progress.
So far, though, policymakers are ignoring the lessons of the past. At the state level, California, Colorado and New York began implementing influential legislation this year. Last December, the current administration issued an executive order that claims to preempt some state laws.