Unintended biases in machine learning (ML) models are among the major concerns that must be addressed to maintain public trust in ML. In this paper, we address process fairness of ML models that consists in reducing the dependence of models on sensitive features, without compromising their performance. We revisit the framework FixOut that is inspired in the approach "fairness through unawareness" to build fairer models. We introduce several improvements such as automating the choice of FixOut's parameters. Also, FixOut was originally proposed to improve fairness of ML models on tabular data. We also demonstrate the feasibility of FixOut's workflow for models on textual data. We present several experimental results that illustrate the fact that FixOut improves process fairness on different classification settings.
Towards Benchmarking Foundation Models for Tabular Data With Text
Department of Computer Science, University of Freiburg, Freiburg, Germany（弗赖堡大学计算机科学系）；Prior Labs, Freiburg, Germany（Prior实验室）；ELLIS Institute, Tubingen, Germany（图宾根ELLIS研究所）
Small Models are LLM Knowledge Triggers on Medical Tabular Prediction
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Thrust of Artificial Intelligence, Information Hub, HKUST (GZ)（香港科技大学（广州）信息枢纽人工智能方向）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；The Second Affiliated Hospital Zhejiang University School of Medicine（浙江大学医学院附属第二医院）；Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence（浙江省医学影像人工智能重点实验室）
JoLT: Joint Probabilistic Predictions on Tabular Data Using LLMs
University of Cambridge（剑桥大学）；University of Toronto（多伦多大学）；The Alan Turing Institute（艾伦·图灵研究所）；Vector Institute（向量研究所）；Microsoft Research Cambridge（微软研究院剑桥分部）
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；CSIRO’s Data61（CSIRO数据61）；Monash University（墨尔本大学）；International Center of Future Science, Jilin University（吉林大学未来科学国际中心）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE, China（知识驱动人机智能工程研究中心，教育部，中国）
