The Case for Regulating AI Applications, Not Algorithms
📷 Image source: spectrum.ieee.org
A Call for Nuance in AI Governance
Why targeting the use of artificial intelligence is more effective than restricting the models themselves
The global rush to regulate artificial intelligence has often focused on the powerful models themselves—the complex algorithms trained on vast datasets. But a growing chorus of experts argues this approach is fundamentally misdirected. According to spectrum.ieee.org, the more effective and less stifling path is to regulate specific, high-risk *uses* of AI, not the underlying technology. This perspective, detailed in a report from the publication, suggests that laws should target concrete harms in specific contexts, much like we regulate the unsafe use of a car rather than the engineering of its engine.
This distinction is critical for fostering innovation while protecting society. A model capable of generating text or analyzing images is, in essence, a multipurpose tool. Its potential for harm or benefit is entirely determined by how it is deployed. By shifting the regulatory lens to application, policymakers can address real-world risks—like algorithmic bias in hiring or misinformation in political campaigns—without placing premature constraints on a rapidly evolving technological field.
The Inherent Challenge of Regulating a General-Purpose Technology
Artificial intelligence, particularly large language and multimodal models, is what economists term a general-purpose technology. Its core innovation is adaptability. The same foundational model can be fine-tuned to draft medical summaries, write poetry, or generate computer code. Regulating the model itself is akin to creating rules for 'chemistry' rather than for 'toxic waste disposal' or 'pharmaceutical safety.' The report on spectrum.ieee.org emphasizes that this broad-brush approach is both impractical and counterproductive.
Attempting to govern the model architecture or training data directly leads to a regulatory quagmire. How does a regulator pre-emptively evaluate every potential use of a model with millions or billions of parameters? A strict rule applied to the model's development could inadvertently cripple beneficial applications in scientific research or creative arts, all in the name of preventing misuse in an entirely different sector. The focus, therefore, must be downstream, where intention and impact become clear.
Precedent in Existing Law: The Use-Case Framework
Learning from how society manages other dual-use technologies
Society already has a robust playbook for managing technologies that can be used for both good and ill. Consider a sharp kitchen knife. Its design and sale are not heavily regulated, but its use in a violent act is clearly illegal. Similarly, cryptography is a powerful tool for securing financial transactions and private communications; its export was once heavily controlled due to national security concerns, but its development was not banned.
The report draws a parallel to this established legal philosophy. According to spectrum.ieee.org, regulating AI use cases allows for rules that are proportionate to the risk. A chatbot providing mental health advice would rightly face stricter scrutiny and different requirements than a chatbot recommending recipes. This sector-specific approach leverages existing domain expertise—in healthcare, finance, or criminal justice—to craft sensible guardrails that address actual harms without imposing a one-size-fits-all burden on AI developers.
The Pitfalls of Model-Centric Regulation
A regulatory strategy focused on the models themselves presents several clear dangers. First, it risks cementing the dominance of a few large tech companies. Onerous compliance costs for training and deploying large models could be absorbed by giants like Google or Meta but would be prohibitive for startups and academic researchers, stifling competition and diversity in the field.
Second, it could easily become obsolete. AI architecture is evolving at a breakneck pace. A law written today targeting specific model characteristics might be irrelevant or obstructive to next year's breakthroughs. Finally, it creates a false sense of security. A 'regulated' model approved for release is not inherently safe; its safety is contingent on its application. A model certified for educational use could be repurposed for generating disinformation, rendering the initial model-level approval meaningless without ongoing oversight of its deployment.
Defining High-Risk Use Cases for Targeted Scrutiny
So, what constitutes a high-risk use case worthy of regulatory intervention? The report suggests looking at applications where AI makes or supports significant decisions affecting human rights, safety, or access to essential services. Prime examples include AI used in judicial sentencing, credit scoring, autonomous vehicle navigation, medical diagnosis, and critical infrastructure management.
For these applications, regulators could mandate rigorous testing for bias, robustness, and safety before deployment. They could require transparency—informing users when they are interacting with an AI system—and ensure clear human oversight and accountability mechanisms. According to spectrum.ieee.org, this targeted framework is more manageable for regulators to enforce and for companies to comply with, as the requirements are tied to a known product with a defined purpose, not an amorphous, ever-changing base technology.
The Role of Developers in a Use-Regulated Ecosystem
This does not absolve AI developers of responsibility. In a use-case regulatory regime, model creators would have a duty of care. This could involve conducting and documenting rigorous pre-training data audits, implementing state-of-the-art security to prevent model theft or misuse, and developing tools that allow downstream deployers to better understand and control model behavior.
Think of it as a chain of responsibility. The model builder provides a powerful, well-documented tool. The company that fine-tunes it for a specific purpose, like screening job applicants, is then directly liable for ensuring that application complies with employment discrimination laws. This clear division aligns legal accountability with operational control. The developer influences safety; the deployer is responsible for it in a specific context.
International Implications and the Need for Harmonization
Avoiding a fragmented global landscape for AI application
A shift toward use-case regulation could also ease the path to international consensus. Agreeing on global standards for what constitutes a safe AI model is a Herculean task, given differing cultural values and technological capacities. However, finding common ground on specific applications may be more feasible. Most nations can agree that AI in medical devices must be safe and effective, or that algorithmic trading should not destabilize markets.
According to the analysis on spectrum.ieee.org, a use-based framework allows countries to tailor rules to their domestic priorities while cooperating on shared challenges. It prevents a scenario where a model legal in one jurisdiction is banned in another simply for existing, instead focusing regulatory divergence on socially contested applications, where such differences are a normal part of democratic policymaking.
Forging a Practical Path Forward for AI Policy
The debate is not about whether to regulate AI, but how. The argument presented is for intelligent, precise governance that mitigates risk without halting progress. By regulating AI use, policymakers can protect citizens from tangible harms—discriminatory outcomes, safety failures, and fraud—while allowing the underlying science of artificial intelligence to flourish.
This approach acknowledges a simple truth: technology itself is rarely the problem. It is human intention and action that determine its impact. Effective law has always sought to guide that intention and action. As the report concludes, the future of AI should be shaped by rules that govern what we *do* with these remarkable tools, not by preemptive restrictions on the tools themselves. The path forward requires vigilance, not fear, and regulation that is as adaptable and targeted as the technology it aims to steward.
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