发表机构
Scheller College of Business, Georgia Tech（舍勒商学院，佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究指出机器学习模型解释存在准确性与可解释性权衡非根本性的观点，引入罗生门解释范式，提出RashomonLLM工作流程，经实验验证其在多任务中显著优于基线，提升业务性能并奠定消费者信任基础。
AI 中文摘要
解释机器学习模型对于决策和消费者信任愈发重要，但人们普遍认为这要付出代价，即现有可解释人工智能（XAI）方法存在持续的准确性与可解释性权衡。我们认为这种权衡并非根本性的，而是将解释和预测视为单独目标的产物。当适当地结合时，它们会相互补充。我们引入了罗生门解释范式，构建一组忠实且能指导预测的解释，并证明该集合通常非空且解释保真度限制了其指导的模型的性能。为探索这个集合，我们提出了RashomonLLM，一种通过迭代使其与预测对齐来生成自然语言解释的解释 - 预测 - 反思代理工作流程，并证明它会收敛并恢复完整集合。在客户流失分类、临床生存回归和大规模直播日志的工业点击率预测中，RashomonLLM在准确性和解释质量上均显著优于现有预测和XAI基线，其增益由解释保真度驱动，且对分布变化、时间分割和种子具有鲁棒性。我们的框架提高了业务性能并为消费者信任奠定了基础。
英文摘要
Explaining machine-learning models is increasingly important for decision-making and consumer trust, yet it is widely believed to come at a cost: existing Explainable AI (XAI) methods suffer from a persistent accuracy-explainability trade-off. We argue that this trade-off is not fundamental, but an artifact of treating explanation and prediction as separate objectives; when properly coupled, they become complementary, so that equipping a model to explain itself improves, rather than degrades, its accuracy. We introduce the Rashomon Explanation paradigm, which builds a set of faithful, prediction-guiding explanations rather than a single one, and prove that this set is generally non-empty and that explanation fidelity bounds the performance of the models it guides. To explore this set, we propose RashomonLLM, an Explanation-Prediction-Reflection agentic workflow that generates explanations in natural language by iteratively aligning them with predictions, and we prove it converges and recovers the full set. Across customer-churn classification, clinical survival regression, and industrial click-through prediction on large-scale live-streaming logs, RashomonLLM significantly outperforms state-of-the-art prediction and XAI baselines on both accuracy and explanation quality, with gains driven by explanation fidelity and robust to distribution shifts, temporal splits, and seeds. Our framework thus advances business performance while laying the groundwork for consumer trust.
