亚马逊公司(Amazon,简称亚马逊;NASDAQ:AMZN),是美国最大的一家网络电子商务公司,位于华盛顿州的西雅图。是网络上最早开始经营电子商务的公司之一,亚马逊成立于1995年,一开始只经营网络的书籍销售业务,现在则扩及了范围相当广的其他产品,已成为全球商品品种最多的网上零售商和全球第二大互联网企业,在公司名下,也包括了AlexaInternet、a9、lab126、和互联网电影数据库(Internet Movie Database,IMDB)等子公司。

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机器学习模型和数据驱动系统正越来越多地用于帮助在金融服务、医疗保健、教育和人力资源等领域做出决策。机器学习应用程序提供了诸如提高准确性、提高生产率和节约成本等好处。这一趋势是多种因素共同作用的结果,最显著的是无处不在的连通性、使用云计算收集、聚合和处理大量细粒度数据的能力,以及对能够分析这些数据的日益复杂的机器学习模型的更好访问。

开发负责任的人工智能解决方案是一个过程,涉及在人工智能生命周期的所有阶段与关键利益相关者(包括产品、政策、法律、工程和人工智能/ML团队,以及最终用户和社区)进行输入和讨论。在本文中,我们主要关注ML生命周期中用于偏见和可解释性的技术工具。我们还提供了一个简短的章节,介绍了AI公平性和可解释性的限制和最佳实践。

https://pages.awscloud.com/rs/112-TZM-766/images/Amazon.AI.Fairness.and.Explainability.Whitepaper.pdf

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Session-based recommendation which has been witnessed a booming interest recently, focuses on predicting a user's next interested item(s) based on an anonymous session. Most existing studies adopt complex deep learning techniques (e.g., graph neural networks) for effective session-based recommendation. However, they merely address co-occurrence between items, but fail to well distinguish causality and correlation relationship. Considering the varied interpretations and characteristics of causality and correlation relationship between items, in this study, we propose a novel method denoted as CGSR by jointly modeling causality and correlation relationship between items. In particular, we construct cause, effect and correlation graphs from sessions by simultaneously considering the false causality problem. We further design a graph neural network-based method for session-based recommendation. Extensive experiments on three datasets show that our model outperforms other state-of-the-art methods in terms of recommendation accuracy. Moreover, we further propose an explainable framework on CGSR, and demonstrate the explainability of our model via case studies on Amazon dataset.

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Session-based recommendation which has been witnessed a booming interest recently, focuses on predicting a user's next interested item(s) based on an anonymous session. Most existing studies adopt complex deep learning techniques (e.g., graph neural networks) for effective session-based recommendation. However, they merely address co-occurrence between items, but fail to well distinguish causality and correlation relationship. Considering the varied interpretations and characteristics of causality and correlation relationship between items, in this study, we propose a novel method denoted as CGSR by jointly modeling causality and correlation relationship between items. In particular, we construct cause, effect and correlation graphs from sessions by simultaneously considering the false causality problem. We further design a graph neural network-based method for session-based recommendation. Extensive experiments on three datasets show that our model outperforms other state-of-the-art methods in terms of recommendation accuracy. Moreover, we further propose an explainable framework on CGSR, and demonstrate the explainability of our model via case studies on Amazon dataset.

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