基于布尔规则推理的用户驱动模型调整
User Driven Model Adjustment via Boolean Rule Explanations
摘要：
人工智能解决方案在很大程度上依赖于输入训练数据的质量和准确性，然而，训练数据可能并不总是完全反映最新的策略环境，或者可能缺少业务逻辑。可解释性的进步为用户提供了与机器学习预测的可解释进行交互的可能性，以便注入更准确地反映系统当前现实的修改或约束。在本文中，我们提出了一种解决方案，该解决方案利用ML模型的预测能力，同时允许用户指定对决策边界的修改。我们的交互式叠加方法实现了这一目标，而不需要模型重新训练，使其适用于需要即时更改决策的系统。我们证明了用户反馈规则可以与ML预测分层，以提供即时的变化，从而支持使用更少的数据进行学习。
英文摘要：
AI solutions are heavily dependant on the quality and accuracy of the input training data, however the training data may not always fully reflect the most up-to-date policy landscape or may be missing business logic. The advances in explainability have opened the possibility of allowing users to interact with interpretable explanations of ML predictions in order to inject modifications or constraints that more accurately reflect current realities of the system. In this paper, we present a solution which leverages the predictive power of ML models while allowing the user to specify modifications to decision boundaries. Our interactive overlay approach achieves this goal without requiring model retraining, making it appropriate for systems that need to apply instant changes to their decision making. We demonstrate that user feedback rules can be layered with the ML predictions to provide immediate changes which in turn supports learning with less data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
