论文标题
基于改善NTL检测的解释性的人类在环境方法
A Human-in-the-Loop Approach based on Explainability to Improve NTL Detection
论文作者
论文摘要
实施基于机器学习来检测欺诈和其他非技术损失（NTL）的系统具有挑战性：可用数据是有偏见的，当前使用的算法是利益相关者无法轻松信任或理解的黑盒。这项工作解释了我们在真正的系统中减轻这些问题的人类方法，该系统使用监督模型来检测西班牙国际公用事业公司的非技术损失（NTL）。这种方法利用了人类的知识（例如，从数据科学家或公司的利益相关者那里）以及通过解释方法提供的信息来指导系统在培训过程中。这种简单，高效的方法可以在其他工业项目中轻松实现，在真实的数据集中进行了测试，结果表明，在准确性，可解释性，鲁棒性和灵活性方面，派生的预测模型更好。
Implementing systems based on Machine Learning to detect fraud and other Non-Technical Losses (NTL) is challenging: the data available is biased, and the algorithms currently used are black-boxes that cannot be either easily trusted or understood by stakeholders. This work explains our human-in-the-loop approach to mitigate these problems in a real system that uses a supervised model to detect Non-Technical Losses (NTL) for an international utility company from Spain. This approach exploits human knowledge (e.g. from the data scientists or the company's stakeholders) and the information provided by explanatory methods to guide the system during the training process. This simple, efficient method that can be easily implemented in other industrial projects is tested in a real dataset and the results show that the derived prediction model is better in terms of accuracy, interpretability, robustness and flexibility.
