Machine learning methods based on AdaBoost have been widely applied to various classification problems across many mission-critical applications including healthcare, law and finance. However, there is a growing concern about the unfairness and discrimination of data-driven classification models, which is inevitable for classical algorithms including AdaBoost. In order to achieve fair classification, a novel fair AdaBoost (FAB) approach is proposed that is an interpretable fairness-improving variant of AdaBoost. We mainly investigate binary classification problems and focus on the fairness of three different indicators (i.e., accuracy, false positive rate and false negative rate). By utilizing a fairness-aware reweighting technique for base classifiers, the proposed FAB approach can achieve fair classification while maintaining the advantage of AdaBoost with negligible sacrifice of predictive performance. In addition, a hyperparameter is introduced in FAB to show preferences for the fairness-accuracy trade-off. An upper bound for the target loss function that quantifies error rate and unfairness is theoretically derived for FAB, which provides a strict theoretical support for the fairness-improving methods designed for AdaBoost. The effectiveness of the proposed method is demonstrated on three real-world datasets (i.e., Adult, COMPAS and HSLS) with respect to the three fairness indicators. The results are accordant with theoretic analyses, and show that (i) FAB significantly improves classification fairness at a small cost of accuracy compared with AdaBoost; and (ii) FAB outperforms state-of-the-art fair classification methods including equalized odds method, exponentiated gradient method, and disparate mistreatment method in terms of the fairness-accuracy trade-off.
Weighting What Matters: Boosting Sample Efficiency in Medical Report Generation via Token Reweighting
TUM School of Computation, Information and Technology（慕尼黑技术大学计算、信息与技术学院）；Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Department of Computing, Imperial College London（伦敦帝国学院计算机系）
PLA Strategy Support Force Information Engineering University（中国人民解放军战略支援部队信息工程大学）
The Graph Neural Network (GNN) has been widely used for graph data representation. However, the existing researches only consider the ideal balanced dataset, and the imbalanced dataset is rarely considered. Traditional methods such as resampling, reweighting, and synthetic samples that deal with imbalanced datasets are no longer applicable in GNN. This paper proposes an ensemble model called Boosting-GNN, which uses GNNs as the base classifiers during boosting. In Boosting-GNN, higher weights are set for the training samples that are not correctly classified by the previous classifier, thus ac…
Alpha and Prejudice: Improving $α$-sized Worst-case Fairness via Intrinsic Reweighting
Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）；Centre for Frontier AI Research, Agency for Science, Technology and Research（新加坡科技研究局前沿人工智能研究中心）；School of Computer Science, Beihang University（北京航空航天大学计算机学院）
Fair classification has become an important topic in machine learning research. While most bias mitigation strategies focus on neural networks, we noticed a lack of work on fair classifiers based on decision trees even though they have proven very efficient. In an up-to-date comparison of state-of-the-art classification algorithms in tabular data, tree boosting outperforms deep learning. For this reason, we have developed a novel approach of adversarial gradient tree boosting. The objective of the algorithm is to predict the output $Y$ with gradient tree boosting while minimizing the ability o…
Projected Boosting with Fairness Constraints: Quantifying the Cost of Fair Training Distributions
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA（生物统计学系，范德比尔特大学医学中心）；Department of Informatics, King's College London, WC2R 2LS London, UK（信息学系，伦敦国王学院）
University of Michigan（密歇根大学）；ShanghaiTech University（上海科技大学）；IBM Research（IBM研究院）；MIT-IBM Watson AI Lab（MIT-IBM沃森人工智能实验室）
We consider the task of enforcing individual fairness in gradient boosting. Gradient boosting is a popular method for machine learning from tabular data, which arise often in applications where algorithmic fairness is a concern. At a high level, our approach is a functional gradient descent on a (distributionally) robust loss function that encodes our intuition of algorithmic fairness for the ML task at hand. Unlike prior approaches to individual fairness that only work with smooth ML models, our approach also works with non-smooth models such as decision trees. We show that our algorithm conv…
National University of Singapore（新加坡国立大学）；Tsinghua University（清华大学）；Renmin University of China（中国人民大学）；University of Science and Technology of China（中国科学技术大学）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）
Under stringent model type and variable distribution assumptions, differentiable score-based causal discovery methods learn a directed acyclic graph (DAG) from observational data by evaluating candidate graphs over an average score function. Despite great success in low-dimensional linear systems, it has been observed that these approaches overly exploit easier-to-fit samples, thus inevitably learning spurious edges. Worse still, inherent mostly in these methods the common homogeneity assumption can be easily violated, due to the widespread existence of heterogeneous data in the real world, re…
Feedzai（Feedzai公司）；MPI for Intelligent Systems（马克斯·普朗克智能系统研究所）；UC Irvine（加州大学欧文分校）
Tabular data is prevalent in many high-stakes domains, such as financial services or public policy. Gradient Boosted Decision Trees (GBDT) are popular in these settings due to their scalability, performance, and low training cost. While fairness in these domains is a foremost concern, existing in-processing Fair ML methods are either incompatible with GBDT, or incur in significant performance losses while taking considerably longer to train. We present FairGBM, a dual ascent learning framework for training GBDT under fairness constraints, with little to no impact on predictive performance when…
IFFair: Influence Function-driven Sample Reweighting for Fair Classification
East China Normal University（东华师范大学）；Shanghai Key Laboratory of Trustworthy Computing（上海可信计算实验室）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education（教育部符号计算与知识工程重点实验室）
M$^3$Fair: Mitigating Bias in Healthcare Data through Multi-Level and Multi-Sensitive-Attribute Reweighting Method
Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；National Engineering Research Center for Software Engineering, Peking University（北京大学国家软件工程研究中心）；School of Software, Beihang University（北京航空航天大学软件学院）；Department of Electrical and Computer Engineering, North Carolina State University（北卡罗来纳州立大学电气与计算机工程系）；Institute of Genetics and Cancer, University of Edinburgh（爱丁堡大学遗传与癌症研究所）；Health Data Research UK（英国健康数据研究中心）；Department of Nephrology, Peking University Third Hospital（北京大学第三医院肾内科）
Boosting Fair Classifier Generalization through Adaptive Priority Reweighing
University of Science and Technology of China（中国科学技术大学）；Warwick Business School, University of Warwick（华威大学华威商学院）；New Jersey Institute of Technology（新泽西理工学院）；University of Oxford（牛津大学）；University of Edinburgh（爱丁堡大学）
Machine learning models are trained to minimize the mean loss for a single metric, and thus typically do not consider fairness and robustness. Neglecting such metrics in training can make these models prone to fairness violations when training data are imbalanced or test distributions differ. This work introduces Fairness Optimized Reweighting via Meta-Learning (FORML), a training algorithm that balances fairness and robustness with accuracy by jointly learning training sample weights and neural network parameters. The approach increases model fairness by learning to balance the contributions …
Leibniz University Hannover（汉诺威莱布尼茨大学）；L3S Research Center（L3S研究中心）；Freie Universität Berlin（柏林自由大学）
Data-driven AI systems can lead to discrimination on the basis of protected attributes like gender or race. One reason for this behavior is the encoded societal biases in the training data (e.g., females are underrepresented), which is aggravated in the presence of unbalanced class distributions (e.g., "granted" is the minority class). State-of-the-art fairness-aware machine learning approaches focus on preserving the \emph{overall} classification accuracy while improving fairness. In the presence of class-imbalance, such methods may further aggravate the problem of discrimination by denying a…
Individual Fairness Through Reweighting and Tuning
Stanford Center for Biomedical Ethics, Stanford University（斯坦福大学生物医学伦理中心）；Artificial Intelligence and Machine Learning, GSK（葛兰素史克人工智能与机器学习部门）；Department of Biomedical Data Science, Stanford University（斯坦福大学生物医学数据科学系）；Department of Bioengineering, Stanford University（斯坦福大学生物工程系）；Department of Genetics, Stanford University（斯坦福大学遗传学系）；Department of Medicine, Stanford University（斯坦福大学医学系）
本文针对 GLR 个体公平性方法依赖训练时目标数据的问题，提出在训练与目标数据上独立定义 GLR，并用 NFG 衡量公平增益；实验表明其性能与原方法相当，且 PC 可能误导公平性评估。
L3S Research Center, Leibniz University of Hannover（汉诺威莱布尼茨大学L3S研究中心）；Leibniz University of Hannover（汉诺威莱布尼茨大学）
The widespread use of ML-based decision making in domains with high societal impact such as recidivism, job hiring and loan credit has raised a lot of concerns regarding potential discrimination. In particular, in certain cases it has been observed that ML algorithms can provide different decisions based on sensitive attributes such as gender or race and therefore can lead to discrimination. Although, several fairness-aware ML approaches have been proposed, their focus has been largely on preserving the overall classification accuracy while improving fairness in predictions for both protected …
University of Belgrade - Faculty of Organizational Sciences（贝尔格莱德大学组织科学学院）；University of Belgrade - Faculty of Mathematics（贝尔格莱德大学数学学院）
With growing awareness of societal impact of artificial intelligence, fairness has become an important aspect of machine learning algorithms. The issue is that human biases towards certain groups of population, defined by sensitive features like race and gender, are introduced to the training data through data collection and labeling. Two important directions of fairness ensuring research have focused on (i) instance weighting in order to decrease the impact of more biased instances and (ii) adversarial training in order to construct data representations informative of the target variable, but…
U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection
University of Cambridge（剑桥大学）；the Alan Turing Institute（艾伦·图灵研究所）；Indian Institute of Technology Kanpur（印度理工学院坎普尔分校）；Middle East Technical University (METU)（中东技术大学）
An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee
South China University of Technology（华南理工大学）；the University of Warwick（华威大学）；State University of New York, New Paltz（纽约州立大学新帕尔兹分校）；The University of Sydney（悉尼大学）
Georgia Tech（佐治亚理工学院）；Google（谷歌）；Amazon Web Services（亚马逊云科技）；University of Washington（华盛顿大学）
We propose simple active sampling and reweighting strategies for optimizing min-max fairness that can be applied to any classification or regression model learned via loss minimization. The key intuition behind our approach is to use at each timestep a datapoint from the group that is worst off under the current model for updating the model. The ease of implementation and the generality of our robust formulation make it an attractive option for improving model performance on disadvantaged groups. For convex learning problems, such as linear or logistic regression, we provide a fine-grained ana…
Isomorphic Functionalities between Ant Colony and Ensemble Learning: Part II-On the Strength of Weak Learnability and the Boosting Paradigm
School of Mathematics and Statistics, Rochester Institute of Technology（罗彻斯特理工学院数学与统计学院）；Gosnell School of Life Sciences, Rochester Institute of Technology（罗彻斯特理工学院戈斯内尔生命科学学院）；Center for Imaging Science, Rochester Institute of Technology（罗彻斯特理工学院成像科学中心）
Fix Representation (Optimally) Before Fairness: Finite-Sample Shrinkage Population Correction and the True Price of Fairness Under Subpopulation Shift
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA（生物统计学系，范德比尔特大学医学中心）；Department of Informatics, King's College London, WC2R 2LS London, UK（信息学系，国王学院伦敦）
Indian Institute of Technology, Ropar（印度理工学院鲁帕尔分校）
The observed ratings in most recommender systems are subjected to popularity bias and are thus not randomly missing. Due to this, only a few popular items are recommended, and a vast number of non-popular items are hardly recommended. Not suggesting the non-popular items lead to fewer products dominating the market and thus offering fewer opportunities for creativity and innovation. In the literature, several fair algorithms have been proposed which mainly focused on improving the accuracy of the recommendation system. However, a typical accuracy measure is biased towards popular items, i.e., …
Recent work in fairness in machine learning has proposed adjusting for fairness by equalizing accuracy metrics across groups and has also studied how datasets affected by historical prejudices may lead to unfair decision policies. We connect these lines of work and study the residual unfairness that arises when a fairness-adjusted predictor is not actually fair on the target population due to systematic censoring of training data by existing biased policies. This scenario is particularly common in the same applications where fairness is a concern. We characterize theoretically the impact of su…
Amazon Web Services（亚马逊云科技）；Carnegie Mellon University（卡内基梅隆大学）
Given the increasing importance of machine learning (ML) in our lives, several algorithmic fairness techniques have been proposed to mitigate biases in the outcomes of the ML models. However, most of these techniques are specialized to cater to a single family of ML models and a specific definition of fairness, limiting their adaptibility in practice. We introduce a general constrained Bayesian optimization (BO) framework to optimize the performance of any ML model while enforcing one or multiple fairness constraints. BO is a model-agnostic optimization method that has been successfully applie…
Australian National University（澳大利亚国立大学）；Google Research（谷歌研究院）；Stanford University（斯坦福大学）；Tel Aviv University（特拉维夫大学）；Data61/CSIRO（Data61/联邦科学与工业研究组织）
We introduce a new family of techniques to post-process ("wrap") a black-box classifier in order to reduce its bias. Our technique builds on the recent analysis of improper loss functions whose optimization can correct any twist in prediction, unfairness being treated as a twist. In the post-processing, we learn a wrapper function which we define as an $α$-tree, which modifies the prediction. We provide two generic boosting algorithms to learn $α$-trees. We show that our modification has appealing properties in terms of composition of $α$-trees, generalization, interpretability, and KL diverge…
Dealicious Inc.（Dealicious公司）；Institute of Advanced Research on Artificial Intelligence (IARAI)（人工智能高等研究院）；Athena RC（雅典娜研究中心）
Vision transformers have achieved remarkable progress in vision tasks such as image classification and detection. However, in instance-level image retrieval, transformers have not yet shown good performance compared to convolutional networks. We propose a number of improvements that make transformers outperform the state of the art for the first time. (1) We show that a hybrid architecture is more effective than plain transformers, by a large margin. (2) We introduce two branches collecting global (classification token) and local (patch tokens) information, from which we form a global image re…
Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learning
Bowie State University（鲍伊州立大学）；Télécom SudParis（南巴黎电信学院）；Institut Polytechnique de Paris（巴黎综合理工学院）；Brown University（布朗大学）；Northern Virginia Community College（北弗吉尼亚社区学院）
