Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences

Current AI regulations miss the mark on consumer protection: The paper argues that existing regulatory approaches focusing on compute thresholds (how powerful a model is) and generic model tests fail to protect consumers from real-world harms, because they don't evaluate how AI systems actually perform in specific applications that people use.

What matters is real user experience, not just model power: Rather than regulating based on a model's computational size, the authors propose certification should focus on evaluating actual customer-facing experiences—for example, how well a chatbot performs for customer service versus medical advice—using domain-specific test datasets.

High-quality evaluation datasets are key to responsible AI: The paper emphasizes that curated, expert-reviewed datasets tailored to specific use cases are essential for determining whether an AI system is safe and effective, positioning data quality as central to AI regulation rather than a secondary concern.

This approach could balance innovation with safety: By shifting focus from broad compute thresholds to specific use-case evaluations, the framework aims to allow AI companies flexibility to innovate while still ensuring regulators can directly protect consumers from harmful or ineffective AI applications.

Certification could build consumer trust and business credibility: The proposed workflow would enable companies to demonstrate their AI products are genuinely safe and effective for their intended purpose, potentially increasing consumer confidence while giving regulators clearer evidence of responsible AI development.