In high-stakes settings where machine learning models are used to automate decision-making about individuals, the presence of algorithmic bias can exacerbate systemic harm to certain subgroups of people. These biases often stem from the underlying training data. In practice, interventions to "fix the data" depend on the actual additional data sources available -- where many are less than ideal. In these cases, the effects of data scaling on subgroup performance become volatile, as the improvements from increased sample size are counteracted by the introduction of distribution shifts in the training set. In this paper, we investigate the limitations of combining data sources to improve subgroup performance within the context of healthcare. Clinical models are commonly trained on datasets comprised of patient electronic health record (EHR) data from different hospitals or admission departments. Across two such datasets, the eICU Collaborative Research Database and the MIMIC-IV dataset, we find that data addition can both help and hurt model fairness and performance, and many intuitive strategies for data selection are unreliable. We compare model-based post-hoc calibration and data-centric addition strategies to find that the combination of both is important to improve subgroup performance. Our work questions the traditional dogma of "better data" for overcoming fairness challenges by comparing and combining data- and model-based approaches.
Improving Fairness of Large Language Model-Based ICU Mortality Prediction via Case-Based Prompting
School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China（天津理工大学计算机科学与工程学院）；Institute for Artificial Intelligence, Peking University, Beijing, China（北京大学人工智能研究院）；DCST, BNRist, RIIT, Institute of Internet Industry, Tsinghua University, Beijing, China（清华大学信息产业研究院）；National Institute of Health Data Science, Peking University, Beijing, China（北京大学健康数据科学国家研究院）
Investigating the Multilingual Calibration Effects of Language Model Instruction-Tuning
Mila - Quebec AI Institute（魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；The University of Tokyo（东京大学）；Western University（西方大学）；Vector Institute（向量研究所）；Polytechnique Montréal（蒙特利尔理工学院）；CIFAR AI Chair（CIFAR人工智能主席）；AIST（日本产业技术综合研究所）
Beyond Demographic Balance: Multi-Metric and Intersectional Evaluation of Fairness in MIMIC-IV Mortality Prediction
Virginia Tech（弗吉尼亚理工大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；University of Melbourne（墨尔本大学）；University of Toronto（多伦多大学）；University of Virginia（弗吉尼亚大学）；Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）
Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methods
Technical University of Munich（慕尼黑技术大学）；School of Computation, Information and Technology（计算、信息与技术学院）；Department of Computer Science（计算机科学系）；Technische Universität Berlin（柏林技术大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionals
University of Edinburgh（爱丁堡大学）；Inria Bordeaux（波尔多信息与自动化研究院）
本文重新构建 ML 偏见问题，通过创建模型识别英语数据中的偏见而非消除它，并与信息与遗产专业人员合作评估，揭示了 ML 识别偏见的局限性，提出扩展 ML 公平性方法的混合研究路径。
A review of clustering models in educational data science towards fairness-aware learning
L3S Research Center, Leibniz University Hannover（汉诺威莱布尼茨大学L3S研究中心）；Leibniz University Hannover（汉诺威莱布尼茨大学）；Institute for Didactics of Mathematics and Physics, Leibniz University Hannover（汉诺威莱布尼茨大学数学与物理教学研究所）；Research Institute CODE, Bundeswehr University Munich（慕尼黑联邦国防军大学CODE研究所）
Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity
University of Washington（华盛顿大学）；Columbia University（哥伦比亚大学）；University of British Columbia（不列颠哥伦比亚大学）；University of Michigan（密歇根大学）；Cornell University（康奈尔大学）
Making Machine Learning Datasets and Models FAIR for HPC: A Methodology and Case Study
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；California State University, Long Beach（加州州立大学长滩分校）；Argonne National Laboratory（阿贡国家实验室）；University of California Santa Cruz（加州大学圣克鲁兹分校）
Benchmarking Generalist and Specialist Time-Series Foundation Models for PPG-Based Health Assessment
Nell Hodgson Woodruff School of Nursing（Nell Hodgson Woodruff护理学院）；School of Computer Science（计算机科学学院）；Department of Pediatrics（儿科系）；Department of Computer Science（计算机科学系）；Department of Epidemiology（流行病学系）；Department of Anesthesiology（麻醉学系）；Department of Surgery（外科系）；Department of Medicine（医学系）；School of Medicine（医学院）
