论文标题
人工智能对业务流程的信任：对过程感知解释的需求
AI Trust in business processes: The need for process-aware explanations
论文作者
论文摘要
业务流程是大量企业运营的基础，包括处理贷款申请，管理发票和保险索赔。为AI注入降低成本或提供更好的客户体验的机会很大，并且业务流程管理（BPM）文献丰富了机器学习解决方案，包括无监督的学习，以了解过程痕迹的群集，分类模型，以预测部分过程，持续时间，持续时间或部分流程跟踪的途径，从文档中提取业务流程，从文档中提取业务和模型，以推荐或导航业务。最近，将包括NLP领域的深度学习模型应用于处理预测。 不幸的是，企业公司几乎没有应用和采用这些创新。我们断言，在BPM中缺乏AI模型的一个很大原因是，业务用户是规避风险的，并且不会隐含地信任AI模型。不幸的是，很少有人注意向具有流程上下文的企业用户解释模型预测。我们挑战BPM社区以建立AI的可解释性文献，而AI信托社区了解
Business processes underpin a large number of enterprise operations including processing loan applications, managing invoices, and insurance claims. There is a large opportunity for infusing AI to reduce cost or provide better customer experience, and the business process management (BPM) literature is rich in machine learning solutions including unsupervised learning to gain insights on clusters of process traces, classification models to predict the outcomes, duration, or paths of partial process traces, extracting business process from documents, and models to recommend how to optimize a business process or navigate decision points. More recently, deep learning models including those from the NLP domain have been applied to process predictions. Unfortunately, very little of these innovations have been applied and adopted by enterprise companies. We assert that a large reason for the lack of adoption of AI models in BPM is that business users are risk-averse and do not implicitly trust AI models. There has, unfortunately, been little attention paid to explaining model predictions to business users with process context. We challenge the BPM community to build on the AI interpretability literature, and the AI Trust community to understand
