Researchers have developed a new framework for AI service contracts that addresses the issue of moral hazard, where providers might use lower-quality models without detection. The proposed solution extends contract theory to accommodate user-initiated contract termination, framing it as a Principal-Agent game. The optimal contracts are designed to be proportional to anytime-valid hypothesis tests using likelihood-ratio e-values, with an efficient algorithm provided for their computation. The research also explores approximations using betting e-processes for unknown models and evaluates the findings on both synthetic and real-world LLM data. AI
IMPACT Introduces a theoretical framework to ensure quality and fair pricing in AI services, potentially impacting how users interact with and pay for AI.
RANK_REASON Academic paper on a novel theoretical framework for AI service contracts. [lever_c_demoted from research: ic=1 ai=1.0]
- AI services
- arXiv
- betting e-processes
- contract theory
- Hugging Face
- likelihood-ratio e-values
- LLMs
- model providers
- Principal-Agent game
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