BACON: A fully explainable AI model with graded logic for decision making problems

TL;DR

BACON is a novel, fully explainable AI framework using graded logic that achieves high accuracy and transparency in decision-making tasks, facilitating human-AI collaboration and expert refinement.

Contribution

We introduce BACON, a new framework that combines high predictive accuracy with fully transparent, logic-based explanations for decision-making models.

Findings

BACON provides high accuracy across diverse scenarios.

Models are compact and human-verifiable.

Demonstrates effectiveness in real-world decision tasks.

Abstract

As machine learning models and autonomous agents are increasingly deployed in high-stakes, real-world domains such as healthcare, security, finance, and robotics, the need for transparent and trustworthy explanations has become critical. To ensure end-to-end transparency of AI decisions, we need models that are not only accurate but also fully explainable and human-tunable. We introduce BACON, a novel framework for automatically training explainable AI models for decision making problems using graded logic. BACON achieves high predictive accuracy while offering full structural transparency and precise, logic-based symbolic explanations, enabling effective human-AI collaboration and expert-guided refinement. We evaluate BACON with a diverse set of scenarios: classic Boolean approximation, Iris flower classification, house purchasing decisions and breast cancer diagnosis. In each case, BACON provides high-performance models while producing compact, human-verifiable decision logic. These results demonstrate BACON's potential as a practical and principled approach for delivering crisp, trustworthy explainable AI.