Recent studies highlight the effectiveness of using in-context learning (ICL) to steer large language models (LLMs) in processing tabular data, a challenging task given the structured nature of such data. Despite advancements in performance, the fairness implications of these methods are less understood. This study investigates how varying demonstrations within ICL prompts influence the fairness outcomes of LLMs. Our findings reveal that deliberately including minority group samples in prompts significantly boosts fairness without sacrificing predictive accuracy. Further experiments demonstrate that the proportion of minority to majority samples in demonstrations affects the trade-off between fairness and prediction accuracy. Based on these insights, we introduce a mitigation technique that employs clustering and evolutionary strategies to curate a diverse and representative sample set from the training data. This approach aims to enhance both predictive performance and fairness in ICL applications. Experimental results validate that our proposed method dramatically improves fairness across various metrics, showing its efficacy in real-world scenarios.
Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification
Indian Institute of Technology Delhi（印度德里理工学院）；Delhi Technological University（德里理工大学）；Indian Institute of Science Education and Research Kolkata（印度加尔各答科学教育与研究学院）
Partial Fairness Awareness: Belief-Guided Strategic Mechanism for Strategic Agents
National University of Defense Technology, Changsha, China（国防科技大学）；Peking University, Beijing, China（北京大学）；Shanghai University of Finance and Economics, Shanghai, China（上海财经大学）；ZGC Laboratory, Beijing, China（ZGC实验室）；Faculty of Computing, Harbin Institute of Technology, Harbin, China（哈尔滨工业大学计算机学院）
SMITE: Enhancing Fairness in LLMs through Optimal In-Context Example Selection via Dynamic Validation
Indian Institute of Technology Delhi, India（印度德里理工学院）；Delhi Technological University, India（德里技术大学）；Indian Institute of Science Education and Research Kolkata, India（印度科学教育与研究学院科希拉分校）；Indian Institute of Technology Kharagpur, India（印度理工学院哈里科格分校）
Beyond Independent Manipulation: Individual Fairness-aware Strategic Classification with Peer Imitation
College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机科学与技术学院）；School of Mathematical Sciences, Peking University（北京大学数学学院）；Institute for Theoretical Computer Science, Shanghai University of Finance and Economics（上海财经大学理论计算机科学研究所）；Information Technology Development, Aetos Capital Group, Sydney（悉尼Aetos资本集团信息技术部）；Faculty of Computing, Harbin Institute of Technology（哈尔滨工业大学计算机学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
GameBench: Evaluating Strategic Reasoning Abilities of LLM Agents
Olin College of Engineering（奥林工程学院）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；University of Oxford（牛津大学）；University College London（伦敦大学学院）；ShanghaiTech University（上海科技大学）；Columbia University（哥伦比亚大学）；University of Manchester（曼彻斯特大学）
Fairness in Multi-modal Medical Diagnosis with Demonstration Selection
Arizona State University（亚利桑那州立大学）；University of Rochester（罗切斯特大学）；University of Virginia（弗吉尼亚大学）；UCL（伦敦大学学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Central Florida（佛罗里达中央大学）
SAGED: A Holistic Bias-Benchmarking Pipeline for Language Models with Customisable Fairness Calibration
Holistic AI（整体人工智能公司）；University College London（伦敦大学学院）；Stanford University（斯坦福大学）；Center for Long-term AI（长期人工智能中心）；University of Maryland, College Park（马里兰大学帕克分校）；University of California, Berkeley（加州大学伯克利分校）
Demonstrations of Integrity Attacks in Multi-Agent Systems
Shanghai Qi Zhi Institute（上海启智研究院）；Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；University of Pittsburgh（匹兹堡大学）；ShanghaiTech University（上海科技大学）
Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions
Informatics Institute, University of Amsterdam（阿姆斯特丹大学信息研究所）；Department of Computer Science and Technology, SUNY Empire State University（SUNY帝国州大学计算机科学与技术系）；KPMG N.V（KPMG荷兰公司）
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Tübingen AI Center（图宾根人工智能中心）；Department of Computer Science, University of Copenhagen（哥本哈根大学计算机科学系）；ETH Zurich（苏黎世联邦理工学院）
FairContrast: Enhancing Fairness through Contrastive learning and Customized Augmenting Methods on Tabular Data
Department of Industrial Engineering University of Central Florida（工业工程系，中央佛罗里达大学）；Department of Computer Science University of Central Florida（计算机科学系，中央佛罗里达大学）
Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness
CU Boulder（科罗拉多大学博尔德分校）；Columbia University（哥伦比亚大学）；Stanford University（斯坦福大学）；IIT Kharagpur（印度理工学院克勒格布尔分校）；Cornell University（康奈尔大学）；Max Planck Institute for Software Systems（马克斯·普朗克软件系统研究所）
