Organizations are increasingly deploying large language models (LLMs) as customer service agents, decision aids, and semiautonomous agents. We develop State–Understanding–Value–Action (SUVA), a probabilistic auditing framework that turns an LLM’s response into structured evidence about how its decision was produced. SUVA treats the prompt as the state, codes the model’s reasoning to extract its understanding and stated values using a transparent codebook, and then estimates how these elements statistically predict the eventual action. We demonstrate SUVA on social preference games from behavioral economics and show how the same workflow can audit other delegated decisions by using domain-specific prompts and value codebooks. Across eight widely used LLMs, SUVA reveals systematic prosocial and reciprocity patterns and shows how posttraining alignment reshapes them. For practice and policy, SUVA supports a repeatable audit, align, and reaudit workflow for model selection, compliance, and ongoing monitoring of deployed LLM systems.
Leng et al. (Mon,) studied this question.