In medical image diagnosis, fairness has become increasingly crucial. Without bias mitigation, deploying unfair AI would harm the interests of the underprivileged population and potentially tear society apart. Recent research addresses prediction biases in deep learning models concerning demographic groups (e.g., gender, age, and race) by utilizing demographic (sensitive attribute) information during training. However, many sensitive attributes naturally exist in dermatological disease images. If the trained model only targets fairness for a specific attribute, it remains unfair for other attributes. Moreover, training a model that can accommodate multiple sensitive attributes is impractical due to privacy concerns. To overcome this, we propose a method enabling fair predictions for sensitive attributes during the testing phase without using such information during training. Inspired by prior work highlighting the impact of feature entanglement on fairness, we enhance the model features by capturing the features related to the sensitive and target attributes and regularizing the feature entanglement between corresponding classes. This ensures that the model can only classify based on the features related to the target attribute without relying on features associated with sensitive attributes, thereby improving fairness and accuracy. Additionally, we use disease masks from the Segment Anything Model (SAM) to enhance the quality of the learned feature. Experimental results demonstrate that the proposed method can improve fairness in classification compared to state-of-the-art methods in two dermatological disease datasets.
FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRA
Harvard AI and Robotics Lab（哈佛人工智能与机器人实验室）；Harvard University（哈佛大学）；Harvard Ophthalmology AI lab（哈佛眼科人工智能实验室）；University of Central Florida（佛罗里达中央大学）；University of Louisiana at Lafayette（路易斯安那州立大学拉斐特分校）；New York University（纽约大学）；NYUAD Center for Artificial Intelligence and Robotics（纽约大学阿布扎克人工智能与机器人中心）
FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification
Center for Artificial Intelligence and Robotics, New York University Abu Dhabi（纽约大学阿布扎比分校人工智能与机器人中心）；New York University（纽约大学）；Harvard University（哈佛大学）
University of Notre Dame（圣母大学）；University of Pittsburgh（匹兹堡大学）
Dermatological diseases pose a major threat to the global health, affecting almost one-third of the world's population. Various studies have demonstrated that early diagnosis and intervention are often critical to prognosis and outcome. To this end, the past decade has witnessed the rapid evolvement of deep learning based smartphone apps, which allow users to conveniently and timely identify issues that have emerged around their skins. In order to collect sufficient data needed by deep learning and at the same time protect patient privacy, federated learning is often used, where individual cli…
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）
FairAdaBN: Mitigating unfairness with adaptive batch normalization and its application to dermatological disease classification
Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学生命科学与医学部生物医学工程学院）
Guangdong Provincial People’s Hospital（广东省人民医院）；University of Notre Dame（圣母大学）；University of Pittsburgh（匹兹堡大学）
Many works have shown that deep learning-based medical image classification models can exhibit bias toward certain demographic attributes like race, gender, and age. Existing bias mitigation methods primarily focus on learning debiased models, which may not necessarily guarantee all sensitive information can be removed and usually comes with considerable accuracy degradation on both privileged and unprivileged groups. To tackle this issue, we propose a method, FairPrune, that achieves fairness by pruning. Conventionally, pruning is used to reduce the model size for efficient inference. However…
University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；University of Michigan（密歇根大学）
The proliferation of automated face recognition in the commercial and government sectors has caused significant privacy concerns for individuals. One approach to address these privacy concerns is to employ evasion attacks against the metric embedding networks powering face recognition systems: Face obfuscation systems generate imperceptibly perturbed images that cause face recognition systems to misidentify the user. Perturbed faces are generated on metric embedding networks, which are known to be unfair in the context of face recognition. A question of demographic fairness naturally follows: …
FairForensics: Seeing Expressions and Parsing Demographics via Vision-Language Modeling for Generalizable Fair Deepfake Detection
Shenzhen University（深圳大学）；Shandong Artificial Intelligence Institute（山东省人工智能研究院）；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；Tianjin University of Technology（天津理工大学）
Mitigating Group-Level Fairness Disparities in Federated Visual Language Models
Great Bay University（大湾大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Nanyang Technological University（南洋理工大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Shenzhen University（深圳大学）；Pengcheng Laboratory（鹏城实验室）；Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）
Hochschule Darmstadt（达姆施塔特应用技术大学）；Fraunhofer Institute for Computer Graphics Research（弗劳恩霍夫计算机图形学研究所）；TU Darmstadt（达姆施塔特工业大学）
Algorithmic decision systems have frequently been labelled as "biased", "racist", "sexist", or "unfair" by numerous media outlets, organisations, and researchers. There is an ongoing debate whether such assessments are justified and whether citizens and policymakers should be concerned. These and other related matters have recently become a hot topic in the context of biometric technologies, which are ubiquitous in personal, commercial, and governmental applications. Biometrics represent an essential component of many surveillance, access control, and operational identity management systems, t…
Trustworthy and Fair SkinGPT-R1 for Democratizing Dermatological Reasoning across Diverse Ethnicities
School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen)（数据科学学院，香港中文大学（深圳））；Faculty of Information Technology, Monash University（信息技术学院，莫纳什大学）；Department of Dermatology, Tianjin Institute of Integrative Dermatology, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital（皮肤科，天津整合皮肤科研究所，天津中医研究院附属医院）；Department of Dermatology, The First Affiliated Hospital, Shantou University Medical College（皮肤科，汕头大学医学院第一附属医院）；Department of Dermatology, Beijing AnZhen Hospital, Capital Medical University（皮肤科，北京安贞医院，首都医科大学）；School of Computing and Data Science, The University of Hong Kong（计算与数据科学学院，香港大学）；Institute of Automation, Chinese Academy of Sciences（自动化研究所，中国科学院）；Department of Dermatology, Beijing Aerospace General Hospital（皮肤科，北京航天总医院）
Recently, different researchers have found that the gallery composition of a face database can induce performance differentials to facial identification systems in which a probe image is compared against up to all stored reference images to reach a biometric decision. This negative effect is referred to as "watchlist imbalance effect". In this work, we present a method to theoretically estimate said effect for a biometric identification system given its verification performance across demographic groups and the composition of the used gallery. Further, we report results for identification expe…
Decoupling Bias, Aligning Distributions: Synergistic Fairness Optimization for Deepfake Detection
Nanchang University（南昌大学）；Guangdong Provincial Key Laboratory of Intelligent Information Processing（广东省智能信息处理重点实验室）；Shenzhen Key Laboratory of Media Security（深圳媒体安全重点实验室）；SZU–AFS Joint Innovation Center for AI Technology（深圳大学-AFS人工智能技术联合创新中心）；Shenzhen University（深圳大学）；Purdue University（普渡大学）
There has been increasing awareness of ethical issues in machine learning, and fairness has become an important research topic. Most fairness efforts in computer vision have been focused on human sensing applications and preventing discrimination by people's physical attributes such as race, skin color or age by increasing visual representation for particular demographic groups. We argue that ML fairness efforts should extend to object recognition as well. Buildings, artwork, food and clothing are examples of the objects that define human culture. Representing these objects fairly in machine l…
Face biometrics are playing a key role in making modern smart city applications more secure and usable. Commonly, the recognition threshold of a face recognition system is adjusted based on the degree of security for the considered use case. The likelihood of a match can be for instance decreased by setting a high threshold in case of a payment transaction verification. Prior work in face recognition has unfortunately showed that error rates are usually higher for certain demographic groups. These disparities have hence brought into question the fairness of systems empowered with face biometri…
Microsoft AI R&D Center, Taiwan（台湾微软人工智能研发中心）；National Tsing Hua University（国立清华大学）
Although significant progress has been made in face recognition, demographic bias still exists in face recognition systems. For instance, it usually happens that the face recognition performance for a certain demographic group is lower than the others. In this paper, we propose MixFairFace framework to improve the fairness in face recognition models. First of all, we argue that the commonly used attribute-based fairness metric is not appropriate for face recognition. A face recognition system can only be considered fair while every person has a close performance. Hence, we propose a new evalua…
On the Illusion of Gender Bias in Face Recognition: Explaining the Fairness Issue Through Non-demographic Attributes
Paderborn University（帕德博恩大学）；Technical University of Darmstadt（达姆施塔特技术大学）；University of Notre Dame（诺特丹大学）；Johannes Gutenberg University Mainz（美因茨约翰· Gutenberg大学）
While deep learning based approaches have demonstrated expert-level performance in dermatological diagnosis tasks, they have also been shown to exhibit biases toward certain demographic attributes, particularly skin types (e.g., light versus dark), a fairness concern that must be addressed. We propose CIRCLe, a skin color invariant deep representation learning method for improving fairness in skin lesion classification. CIRCLe is trained to classify images by utilizing a regularization loss that encourages images with the same diagnosis but different skin types to have similar latent represent…
Tencent（腾讯）；Nanjing University of Science and Technology（南京理工大学）
Demographic bias is a significant challenge in practical face recognition systems. Existing methods heavily rely on accurate demographic annotations. However, such annotations are usually unavailable in real scenarios. Moreover, these methods are typically designed for a specific demographic group and are not general enough. In this paper, we propose a false positive rate penalty loss, which mitigates face recognition bias by increasing the consistency of instance False Positive Rate (FPR). Specifically, we first define the instance FPR as the ratio between the number of the non-target similar…
TransFair: Transferring Fairness from Ocular Disease Classification to Progression Prediction
School of Computing and Informatics, University of Louisiana at Lafayette（路易斯安那大学拉斐特分校计算与信息学院）；Informatics Research Institute, University of Louisiana at Lafayette（路易斯安那大学拉斐特分校信息学研究所）；College of Engineering and Computer Science, Florida Atlantic University（佛罗里达大西洋大学工程与计算机科学学院）；Harvard Medical School（哈佛医学院）
Redundant Semantic Environment Filling via Misleading-Learning for Fair Deepfake Detection
School of Software, Nanchang University（南昌大学软件学院）；Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, Shenzhen University（广东省智能信息处理重点实验室和深圳媒体安全重点实验室，深圳大学）；Department of Computer and Information Technology, Purdue University（计算机与信息科技系，普渡大学）；Guangdong Laboratory of Machine Perception and Intelligent Computing, Faculty of Engineering, Shenzhen MSU-BIT University（广东机器感知与智能计算实验室，工程学院，深圳MSU-BIT大学）
Institute of Smart Cities（智慧城市研究所）；Public University of Navarre（纳瓦拉公立大学）
The increasing amount of applications of Artificial Intelligence (AI) has led researchers to study the social impact of these technologies and evaluate their fairness. Unfortunately, current fairness metrics are hard to apply in multi-class multi-demographic classification problems, such as Facial Expression Recognition (FER). We propose a new set of metrics to approach these problems. Of the three metrics proposed, two focus on the representational and stereotypical bias of the dataset, and the third one on the residual bias of the trained model. These metrics combined can potentially be used…
