Abstract
The present disclosure provides TrustNet, a behavioral-science-driven consumer trust layer for AI-powered checkout. The system enables autonomous shopping agents to perform product discovery, selection, and payment execution while preserving user control through configurable Trust Dial settings. A Regret Shield functionality, implemented using a regret prediction model, evaluates contemplated purchases using behavioral indicators, spending history, return patterns, merchant information, and transaction attributes to determine whether a transaction should proceed automatically, require confirmation, or be paused. A Glass Box interface provides real-time transparency regarding AI decisions, accessed data, and checkout actions. A SafeNet recovery mechanism supports payment credential validation, backup payment retry, transaction recovery, cool-down periods, and post-purchase reversal. The disclosed framework improves user confidence, reduces regrettable purchases, and enables graduated delegation in agentic commerce environments.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Ezzeddine, Adonis, "TrustNet: A Behavioral-Science-Driven Consumer Trust Layer for AI Powered Checkout with Regret Prediction, Graduated Delegation, and Real Time Transparency", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/12730