We present a gamified explainable AI (XAI) system for ethically aware consumer decision-making in the coffee domain. Each session comprises six rounds with three options per round. Two symbolic engines provide real-time reasons: a Kantian module flags rule violations (e.g., child labor, deforestation risk without shade certification, opaque supply chains, unsafe decaf), and a utilitarian module scores options via multi-criteria aggregation over normalized attributes (price, carbon, water, transparency, farmer income share, taste/freshness, packaging, convenience). A meta-explainer with a regret bound (0.2) highlights Kantian--utilitarian (mis)alignment and switches to a deontically clean, near-parity option when welfare loss is small. We release a structured configuration (attribute schema, certification map, weights, rule set), a policy trace for auditability, and an interactive UI.
@article{arxiv.2510.03892,
title = {Kantian-Utilitarian XAI: Meta-Explained},
author = {Zahra Atf and Peter R. Lewis},
journal= {arXiv preprint arXiv:2510.03892},
year = {2025}
}
Comments
Accepted for presentation as a poster at the 35th IEEE International Conference on Collaborative Advances in Software and Computing, 2025. Conference website:https://conf.researchr.org/details/cascon-2025/posters-track/1/Kantian-Utilitarian-XAI-Meta-Explained