BACON用分级逻辑实现可解释决策，让人类能理解并调整AI判断。
BACON: A fully explainable AI model with graded logic for decision making problems
- 基于分级逻辑自动训练可解释的决策模型，结构透明。
- 在鸢尾花、癌症诊断等任务中达到高准确率且逻辑简洁。
- 适合需要信任和人工干预的医疗、金融等高风险场景。
随着机器学习模型与自主代理在医疗、安全、金融和机器人等高风险领域的广泛应用，透明可信的解释变得至关重要。为实现AI决策的端到端透明，需兼具高精度、完全可解释性与人类可调性。本文提出BACON框架，通过分级逻辑自动训练可解释的决策模型。BACON在保持高预测准确率的同时，提供完整的结构透明性和基于逻辑的符号化解释，支持有效的人机协作与专家引导优化。我们在多个场景中评估：经典布尔近似、鸢尾花分类、房屋购买决策与乳腺癌诊断。在各任务中，BACON均生成高性能模型，并输出紧凑、可人工验证的决策逻辑。结果表明，BACON是一种切实可行且原理严谨的可解释人工智能方法。
原文摘要 · Abstract (English)
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.
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