We propose a fairness-aware learning framework that mitigates intersectional subgroup bias associated with protected attributes. Prior research has primarily focused on mitigating one kind of bias by incorporating complex fairness-driven constraints into optimization objectives or designing additional layers that focus on specific protected attributes. We introduce a simple and generic bias mitigation approach that prevents models from learning relationships between protected attributes and output variable by reducing mutual information between them. We demonstrate that our approach is effective in reducing bias with little or no drop in accuracy. We also show that the models trained with our learning framework become causally fair and insensitive to the values of protected attributes. Finally, we validate our approach by studying feature interactions between protected and non-protected attributes. We demonstrate that these interactions are significantly reduced when applying our bias mitigation.
Research in machine learning fairness has historically considered a single binary demographic attribute; however, the reality is of course far more complicated. In this work, we grapple with questions that arise along three stages of the machine learning pipeline when incorporating intersectionality as multiple demographic attributes: (1) which demographic attributes to include as dataset labels, (2) how to handle the progressively smaller size of subgroups during model training, and (3) how to move beyond existing evaluation metrics when benchmarking model fairness for more subgroups. For eac…
Machine Learning or Artificial Intelligence algorithms have gained considerable scrutiny in recent times owing to their propensity towards imitating and amplifying existing prejudices in society. This has led to a niche but growing body of work that identifies and attempts to fix these biases. A first step towards making these algorithms more fair is designing metrics that measure unfairness. Most existing work in this field deals with either a binary view of fairness (protected vs. unprotected groups) or politically defined categories (race or gender). Such categorization misses the important…
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）
Data Bias Mitigation under Coverage Constraints & The Price of Fairness
Khoury College of Computer Sciences, Northeastern University（东北大学库里计算机科学学院）；Cheriton School of Computer Science, University of Waterloo（滑铁卢大学切里顿计算机科学学院）
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）
The growing capability and accessibility of machine learning has led to its application to many real-world domains and data about people. Despite the benefits algorithmic systems may bring, models can reflect, inject, or exacerbate implicit and explicit societal biases into their outputs, disadvantaging certain demographic subgroups. Discovering which biases a machine learning model has introduced is a great challenge, due to the numerous definitions of fairness and the large number of potentially impacted subgroups. We present FairVis, a mixed-initiative visual analytics system that integrate…
QuantumBlack, a McKinsey company（QuantumBlack，麦肯锡旗下公司）；University of Warwick（华威大学）
Machine learning algorithms are extensively used to make increasingly more consequential decisions about people, so achieving optimal predictive performance can no longer be the only focus. A particularly important consideration is fairness with respect to race, gender, or any other sensitive attribute. This paper studies intersectional fairness, where intersections of multiple sensitive attributes are considered. Prior research has mainly focused on fairness with respect to a single sensitive attribute, with intersectional fairness being comparatively less studied despite its critical importa…
Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software
Nanyang Technological University（南洋理工大学）；Peking University（北京大学）；King’s College London（伦敦国王学院）；University College London（伦敦大学学院）；China-Singapore International Joint Research Institute（中新国际联合研究院）
The issue of fairness in AI has received an increasing amount of attention in recent years. The problem can be approached by looking at different protected attributes (e.g., ethnicity, gender, etc) independently, but fairness for individual protected attributes does not imply intersectional fairness. In this work, we frame the problem of intersectional fairness within a geometrical setting. We project our data onto a hypercube, and split the analysis of fairness by levels, where each level encodes the number of protected attributes we are intersecting over. We prove mathematically that, while …
Machine Learning with Multitype Protected Attributes: Intersectional Fairness through Regularisation
Centre for Actuarial Studies, Department of Economics, University of Melbourne, Australia（墨尔本大学经济系精算研究所以及中心）；Faculty of Economics and Business（经济与商业学院）；University of Amsterdam, The Netherlands（阿姆斯特丹大学）；LRisk, Leuven Research Center on Insurance（勒夫洛保险研究所以及中心）；LStat, Leuven Statistics Research Centre, KU Leuven, Belgium（勒夫洛统计研究所以及中心）
Toward Fairness in Machine Learning Models for Predicting Treatment Retention and Premature Discontinuation in Medication for Opioid Use Disorder
Gary W. Rollins College of Business, The University of Tennessee at Chattanooga（田纳西大学查塔努加分校加里·W·罗林斯商学院）；The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；Texas A&M University（德克萨斯A&M大学）
With the aim of building machine learning systems that incorporate standards of fairness and accountability, we explore explicit subgroup sample complexity bounds. The work is motivated by the observation that classifier predictions for real world datasets often demonstrate drastically different metrics, such as accuracy, when subdivided by specific sensitive variable subgroups. The reasons for these discrepancies are varied and not limited to the influence of mitigating variables, institutional bias, underlying population distributions as well as sampling bias. Among the numerous definitions …
Visa Inc.（Visa公司）；Harvard John A. Paulson School Of Engineering And Applied Sciences（哈佛大学约翰·A·保尔森工程与应用科学学院）；FM: Systems（FM: Systems公司）；Autodesk（Autodesk公司）
Gender classification algorithms have important applications in many domains today such as demographic research, law enforcement, as well as human-computer interaction. Recent research showed that algorithms trained on biased benchmark databases could result in algorithmic bias. However, to date, little research has been carried out on gender classification algorithms' bias towards gender minorities subgroups, such as the LGBTQ and the non-binary population, who have distinct characteristics in gender expression. In this paper, we began by conducting surveys on existing benchmark databases for…
Debiasing Machine Learning Models by Using Weakly Supervised Learning
School of Electrical and Computing Engineering, University of Campinas (UNICAMP)（坎皮纳斯大学电气与计算机工程学院（UNICAMP））；University of New York（纽约大学）；Institut de Mathématiques de Toulouse, Université Paul Sabatier（图卢兹数学研究所，保罗·萨巴蒂埃大学）；Toulouse School of Economics, University of Toulouse Capitole（图卢兹经济学院，图卢兹卡皮托勒大学）；Center of Engineering, Modeling and Applied Social Sciences, Federal University of ABC (UFABC)（ABC联邦大学工程、建模与应用社会科学中心（UFABC））
BiasConnect: Investigating Bias Interactions in Text-to-Image Models
Toyota Technological Institute at Chicago（芝加哥丰田理工学院）；University of British Columbia（不列颠哥伦比亚大学）；Carnegie Mellon University（卡内基梅隆大学）；Emory University（埃默里大学）；University of Pennsylvania（宾夕法尼亚大学）；Indian Institute of Technology Hyderabad（印度理工学院海得拉巴分校）
University of Maryland（马里兰大学）；MPI-SWS（马克斯·普朗克软件系统研究所）
Deep neural networks (DNNs) are increasingly used in real-world applications (e.g. facial recognition). This has resulted in concerns about the fairness of decisions made by these models. Various notions and measures of fairness have been proposed to ensure that a decision-making system does not disproportionately harm (or benefit) particular subgroups of the population. In this paper, we argue that traditional notions of fairness that are only based on models' outputs are not sufficient when the model is vulnerable to adversarial attacks. We argue that in some cases, it may be easier for an a…
University of Edinburgh（爱丁堡大学）；Samsung AI Centre, Cambridge（三星剑桥人工智能中心）
A multitude of work has shown that machine learning-based medical diagnosis systems can be biased against certain subgroups of people. This has motivated a growing number of bias mitigation algorithms that aim to address fairness issues in machine learning. However, it is difficult to compare their effectiveness in medical imaging for two reasons. First, there is little consensus on the criteria to assess fairness. Second, existing bias mitigation algorithms are developed under different settings, e.g., datasets, model selection strategies, backbones, and fairness metrics, making a direct comp…
Fairness of Deep Ensembles: On the interplay between per-group task difficulty and under-representation
CONICET（阿根廷国家科学技术研究委员会）；Universidad Nacional del Litoral（国立利托拉尔大学）；Cohere For AI（Cohere人工智能研究院）；Institute of Computer Sciences, CONICET & Universidad de Buenos Aires（布宜诺斯艾利斯大学计算机科学研究所（隶属于阿根廷国家科学技术研究委员会））
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）
