Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916600074993664 |
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| author | Pasfield, Wesley |
| author_facet | Pasfield, Wesley |
| contents | The rapid advancement of Large Language Models (LLMs) has created a critical gap in consumer protection due to the lack of standardized certification processes for LLM-powered Artificial Intelligence (AI) systems. This paper argues that current regulatory approaches, which focus on compute-level thresholds and generalized model evaluations, are insufficient to ensure the safety and effectiveness of specific LLM-based user experiences. We propose a shift towards a certification process centered on actual user-facing experiences and the curation of high-quality datasets for evaluation. This approach offers several benefits: it drives consumer confidence in AI system performance, enables businesses to demonstrate the credibility of their products, and allows regulators to focus on direct consumer protection. The paper outlines a potential certification workflow, emphasizing the importance of domain-specific datasets and expert evaluation. By repositioning data as the strategic center of regulatory efforts, this framework aims to address the challenges posed by the probabilistic nature of AI systems and the rapid pace of technological advancement. This shift in regulatory focus has the potential to foster innovation while ensuring responsible AI development, ultimately benefiting consumers, businesses, and government entities alike. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_03472 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Pasfield, Wesley Computers and Society The rapid advancement of Large Language Models (LLMs) has created a critical gap in consumer protection due to the lack of standardized certification processes for LLM-powered Artificial Intelligence (AI) systems. This paper argues that current regulatory approaches, which focus on compute-level thresholds and generalized model evaluations, are insufficient to ensure the safety and effectiveness of specific LLM-based user experiences. We propose a shift towards a certification process centered on actual user-facing experiences and the curation of high-quality datasets for evaluation. This approach offers several benefits: it drives consumer confidence in AI system performance, enables businesses to demonstrate the credibility of their products, and allows regulators to focus on direct consumer protection. The paper outlines a potential certification workflow, emphasizing the importance of domain-specific datasets and expert evaluation. By repositioning data as the strategic center of regulatory efforts, this framework aims to address the challenges posed by the probabilistic nature of AI systems and the rapid pace of technological advancement. This shift in regulatory focus has the potential to foster innovation while ensuring responsible AI development, ultimately benefiting consumers, businesses, and government entities alike. |
| title | Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2502.03472 |