Quantifying Trust: Financial Risk Management for Trustworthy AI Agents

2026-04-05

AI Summary

Current research on trustworthy AI predominantly focuses on internal model properties, which proves insufficient for ensuring end-to-end reliability of autonomous AI agents in open environments—particularly regarding task execution, intent alignment, and prevention of tangible harm. This work proposes the Agent Risk Standard (ARS), which, for the first time, adapts underwriting and indemnification mechanisms from financial risk management to AI interactions. By integrating risk modeling, smart contracts, and a transaction settlement framework, ARS transforms trust into quantifiable, enforceable, product-level guarantees. Simulation experiments demonstrate that ARS substantially enhances user protection and social welfare. The project includes an open-source implementation of the proposed standard.

Abstract

Prior work on trustworthy AI emphasizes model-internal properties such as bias mitigation, adversarial robustness, and interpretability. As AI systems evolve into autonomous agents deployed in open environments and increasingly connected to payments or assets, the operational meaning of trust shifts to end-to-end outcomes: whether an agent completes tasks, follows user intent, and avoids failures that cause material or psychological harm. These risks are fundamentally product-level and cannot be eliminated by technical safeguards alone because agent behavior is inherently stochastic. To address this gap between model-level reliability and user-facing assurance, we propose a complementary framework based on risk management. Drawing inspiration from financial underwriting, we introduce the \textbf{Agentic Risk Standard (ARS)}, a payment settlement standard for AI-mediated transactions. ARS integrates risk assessment, underwriting, and compensation into a single transaction framework that protects users when interacting with agents. Under ARS, users receive predefined and contractually enforceable compensation in cases of execution failure, misalignment, or unintended outcomes. This shifts trust from an implicit expectation about model behavior to an explicit, measurable, and enforceable product guarantee. We also present a simulation study analyzing the social benefits of applying ARS to agentic transactions. ARS's implementation can be found at https://github.com/t54-labs/AgenticRiskStandard.

Problem

• Trustworthy AI
• Agentic Risk
• Financial Risk Management
• AI Agents
• Execution Failure

Innovation

• Agentic Risk Standard
• Trustworthy AI
• Financial Risk Management
• AI Agents
• Compensation Framework