It is well known that two-sided markets are unfair in a number of ways. For instance, female workers at Uber earn less than their male colleagues per mile driven. Similar observations have been made for other minority subgroups in other two-sided markets. Here, we suggest a novel market-clearing mechanism for two-sided markets, which promotes equalisation of the pay per hour worked across multiple subgroups, as well as within each subgroup. In the process, we introduce a novel notion of subgroup fairness (which we call Inter-fairness), which can be combined with other notions of fairness within each subgroup (called Intra-fairness), and the utility for the customers (Customer-Care) in the objective of the market-clearing problem. While the novel non-linear terms in the objective complicate market clearing by making the problem non-convex, we show that a certain non-convex augmented Lagrangian relaxation can be approximated to any precision in time polynomial in the number of market participants using semi-definite programming. This makes it possible to implement the market-clearing mechanism efficiently. On the example of driver-ride assignment in an Uber-like system, we demonstrate the efficacy and scalability of the approach, and trade-offs between Inter- and Intra-fairness.
Towards Fair Recommendation in Two-Sided Platforms
Harvard University（哈佛大学）；Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）；L3S Research Center（L3S研究中心）；Max Planck Institute for Software Systems（马克斯·普朗克软件系统研究所）；Indian Institute of Technology Delhi（印度理工学院德里分校）
Looking Beyond What You See: An Empirical Analysis on Subgroup Intersectional Fairness for Multi-label Chest X-ray Classification Using Social Determinants of Racial Health Inequities
Massachusetts Institute of Technology（麻省理工学院）；University at Buffalo（布法罗大学）
Nanyang Technological University（南洋理工大学）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所（IHPC））；Centre for Frontier AI Research (CFAR), Agency for Science, Technology and Research（新加坡科技研究局前沿人工智能研究中心（CFAR））；Beihang University（北京航空航天大学）；New Jersey Institute of Technology（新泽西理工学院）；Zhejiang Sci-Tech University（浙江理工大学）
Ethical Fairness in Ubiquitous Health Sensing without Known Attributes
Ira A. Fulton Schools of Engineering, Arizona State University（亚利桑那州立大学弗里曼工程学院）；Arizona State University（亚利桑那州立大学）；Cornell University（康奈尔大学）；University of Michigan（密歇根大学）
Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃朗根-纽伦堡弗里德里希-亚历山大大学模式识别实验室）；Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen（亚琛大学诊断与介入放射科）；Department of Radiology, Stanford University（斯坦福大学放射科）；Department of Urology, Stanford University（斯坦福大学泌尿科）
Intersectional Fairness in Vision-Language Models for Medical Image Disease Classification
Biomedical Data Analysis and Visualisation (BDAV) Lab, School of Computer Science（生物医学数据分析与可视化实验室，计算机科学学院）；Sydney School of Public Health（悉尼公共卫生学院）；School of Computing（计算学院）
DuetMoE: Coupling Inter- and Intra-Subgroup Robustness for Fair Medical Image Analysis
Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School（先进医学计算与分析中心，麻省总医院和哈佛医学院）；Department of Industrial Engineering, University of Pittsburgh（工业工程系，匹兹堡大学）；Department of Radiation Oncology, College of Medicine, Yonsei University（放射肿瘤学系，延世大学医学院）；Institute for Innovation in Digital Healthcare, Yonsei University（数字医疗创新研究所，延世大学）；Department of Biomedical Systems Informatics, College of Medicine, Yonsei University（生物医学系统信息学系，延世大学医学院）
