2025/05/24 by Guoheng Sun, Sun, Guoheng, Ziyao Wang +11 · 3 citations
Business, Management and Accounting · Social Sciences · #Artificial Intelligence (cs.AI) #Corporate Insolvency and Governance #Cryptography and Security (cs.CR) #Digital Economy and Work Transformation #FOS: Computer and information sciences #Securities Regulation and Market Practices
paper · pdf · doi:10.48550/arxiv.2505.18471
openalex publication_date 2025/05/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Modern large language model (LLM) services increasingly rely on complex, often abstract operations, such as multi-step reasoning and multi-agent collaboration, to generate high-quality outputs. While users are billed based on token consumption and API usage, these internal steps are typically not visible. We refer to such systems as Commercial Opaque LLM Services (COLS). This position paper highlights emerging accountability challenges in COLS: users are billed for operations they cannot observe, verify, or contest. We formalize two key risks: quantity inflation, where token and call counts may be artificially inflated, and quality downgrade, where providers might quietly substitute lower-cost models or tools. Addressing these risks requires a diverse set of auditing strategies, including commitment-based, predictive, behavioral, and signature-based methods. We further explore the potential of complementary mechanisms such as watermarking and trusted execution environments to enhance verifiability without compromising provider confidentiality. We also propose a modular three-layer auditing framework for COLS and users that enables trustworthy verification across execution, secure logging, and user-facing auditability without exposing proprietary internals. Our aim is to encourage further research and policy development toward transparency, auditability, and accountability in commercial LLM services.