论文标题
致力于金融交易的AI
Towards Responsible AI for Financial Transactions
论文作者
论文摘要
AI在金融中的应用越来越依赖于负责人AI的原则。这些原则 - 解释性，公平性，隐私，问责制，透明度和健全性构成了对未来AI系统的信任的基础。在这项研究中，我们通过为深层神经网络提供解释来解决第一个原则，该网络接受了金融交易分类的数字，分类和文本输入的混合。通过（1）使用Shapley添加说明（SHAP）和（2）文本聚类和决策树分类器的混合方法来实现解释。然后，我们通过将模型暴露于靶向逃避攻击中来测试模型的鲁棒性，从而利用我们通过提取的解释获得了有关模型的知识。
The application of AI in finance is increasingly dependent on the principles of responsible AI. These principles - explainability, fairness, privacy, accountability, transparency and soundness form the basis for trust in future AI systems. In this study, we address the first principle by providing an explanation for a deep neural network that is trained on a mixture of numerical, categorical and textual inputs for financial transaction classification. The explanation is achieved through (1) a feature importance analysis using Shapley additive explanations (SHAP) and (2) a hybrid approach of text clustering and decision tree classifiers. We then test the robustness of the model by exposing it to a targeted evasion attack, leveraging the knowledge we gained about the model through the extracted explanation.
