4期

最新文章

• Irrationality-Aware Human Machine Collaboration: Mitigating Alterfactual Irrationality in Copy Trading AI翻译
  作者：Shen, Zhe; Jiang, Wei; Zheng, Zhiqiang (Eric)
  作者单位：Shanghai Jiao Tong University; University of Texas System; University of Texas Dallas
  摘要：The data generated by human decisions inherit human irrationality, yet artificial intelligence (AI) algorithms often operate under the implicit assumption that the data they train on is rational. We challenge this convenient assumption and develop an irrationalityaware human-machine collaboration (IA-HMC) framework to address it. Within this framework, we propose a new concept of alterfactual irrationality, which identifies irrational human decisions influenced by irrelevant alternative inputs...
• Beyond Complements and Substitutes: A Graph Neural Network Approach for Collaborative Retail Sales Forecasting AI翻译
  作者：Liu, Jing; Wang, Gang; Zhao, Huimin; Lu, Mingfeng; Huang, Lihua; Chen, Gang
  作者单位：Tianjin University of Finance & Economics; Hefei University of Technology; University of Wisconsin System; University of Wisconsin Milwaukee; Fudan University
  摘要：Relation-empowered retail management has gained increasing attention. Although evidence on enhanced retail sales forecasting (RSF) for a focal product by leveraging information on related products has been acknowledged, prior studies suffer from high risk of either erroneously introducing irrelevant relations or missing informative ones, as well as incompetence to simultaneously tackle multifaceted and complex product relations. Beyond the well-known relations of complements and substitutes, o...
• Algorithms to the Rescue: Market Mechanisms for Consensual Trading of Unbiased Individual Data AI翻译
  作者：Birkhead, Brian; Eshghi, Ashkan; Gopal, Ram D.; Hidaji, Hooman; Patterson, Raymond A.
  作者单位：University of Warwick; University of Calgary
  摘要：The online economy has relied on collecting and monetizing users' individual data in exchange for tools and services. A lack of transparency and the absence of proper compensation mechanisms have gradually eroded data quality in this market, giving rise to a new generation of platform-mediated data markets that aim to explicitly reimburse data subjects in return for their individual data. Moreover, these platforms can create data markets for direct collection of data from users in contexts lik...
• Healthcare Cost Prediction for Heterogeneous Patient Profiles Using Deep Learning Models with Administrative Claims Data AI翻译
  作者：Morid, Mohammad Amin; Sheng, Olivia R. Liu
  作者单位：Santa Clara University; Arizona State University; Arizona State University-Tempe
  摘要：Accurate and fair patient cost predictions, which can lead to healthcare payer cost savings, are essential to support effective decision making regarding health management policies and resource allocations. Patient cost prediction models utilize administrative claims (AC) data collected from multiple healthcare providers, which payers (e.g., government agencies and private insurance companies) rely on for various reimbursement purposes. Both the variety of patient clinical profiles and the mul...